Launch full-screen from your iPhone / iPad Home Screen
Master Redis 7 from Single-Threaded epoll Event Loops, Cache-Aside Stampede Defense & Rich Data Structures (Strings, Hashes + HEXPIRE, Lists, Sets, SkipList Sorted Sets, Streams) to WATCH/MULTI/EXEC, Atomic Lua Token-Bucket Rate Limiters, RTT Pipelining, RDB/AOF Persistence, ACL Security & 16,384-Slot Redis Cluster Partitioning — with 200 Mock Tests & Voice MockAttempt AI Interviews at ₹2,999 + 18% GST.
Shubham Kale
✓ Enrolled📍 Hyderabad • Backend Eng → Redis 7 Streams
Enrolled: Redis 7 Caching & Streams (REDI)
📅 Weekday Mon–Fri (7–8 PM IST)
Lavanya Murthy
✓ Enrolled📍 Bengaluru • Java Dev → Distributed Caching
Enrolled: Redis 7 Caching & Streams (REDI)
📅 Weekend Sat–Sun (3–5 PM IST)
Aniket Gaikwad
✓ Enrolled📍 Pune • API Engineer → Redis Locks & HA
Enrolled: Redis 7 Caching & Streams (REDI)
📅 Weekday Mon–Fri (8–9 AM IST)
REDI • In-Memory Caching, Distributed Locks & Real-Time Streams
👨💻 Trainers: 100% Active Working IT Professionals
⏱️ Duration: 14–16 Weeks • Live + Hands-On Projects (205h Guided Mastery)
📅 Weekdays (Mon–Fri): 1 Hour / Day • 7:00 AM – 8:00 PM IST (choose any slot based on trainer availability)
📅 Weekends (Sat–Sun): 2 Hours / Day • 7:00 AM – 8:00 PM IST (choose any slot based on trainer availability)
💻 LeetCode & HackerRank: Trainer problem-solving support (Python 3.12 & Atomic Lua).
✓ Live Client Prod Project + Barcode Certificate & Official Internship Letter
Our counselor shares live demo link & issues your Student Portal ID to track every topic transparently.
Click any module or topic below to expand its learning objectives, plain-language overview, real-world purpose, hands-on code, lab tasks, 50-MCQ mocks, MockAttempt AI interviews, and capstone project.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Redis Architecture — Single-Threaded `epoll` Event Loop, I/O Threads, RESP3 & Sub-Millisecond RAM Speed teaches you how Redis applications why **REmote DIctionary Server** achieves `<0.5 ms` p99 latency and `100k+ ops/sec` on a single core: zero mutex lock contention on the main command-execution thread, kernel `epoll`/`kqueue` non-blocking socket multiplexing, Redis 6/7 threaded network socket read/write (`io-threads`), and binary-safe `RESP2`/`RESP3` wire framing. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Architecture — Single-Threaded `epoll` Event Loop, I/O Threads, RESP3 & Sub-Millisecond RAM Speed in real Redis systems — specifically: Why **REmote DIctionary Server** achieves `<0.5 ms` p99 latency and `100k+ ops/sec` on a single core: zero mutex lock contention on the main command-execution thread, kernel `epoll`/`kqueue` non-blocking socket multiplexing, Redis 6/7 threaded network socket read/write (`io-threads`), and binary-safe `RESP2`/`RESP3` wire framing, so your code stays reliable, fast, and easy to maintain in production.
Redis tutorial provides basic and advanced concepts of Redis Database. Our Redis tutorial is designed for beginners and professionals both. Redis is a No SQL database which works on the concept of key-value pair. Our Redis Tutorial includes all topics of Redis such as what is redis, how to install redis, redis commands, data types, keys, lists, strings, hashes, sets, sorted sets, transaction, scripting, connection, s...
Redis Architecture — Single-Threaded `epoll` Event Loop, I/O Threads, RESP3 & Sub-Millisecond RAM Speed — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis Architecture — Single-Threaded `epoll` Event Loop, I/O Threads, RESP3 & Sub-Millisecond R...
Redis Architecture — Single-Threaded `epoll` Event Loop, I/O Threads, RESP3 & Sub-Millisecond RAM Speed — Hands-On Lab Task
class InMemoryRespSim:
def __init__(self) -> None: self._dict: dict[str, str] = {}
def encode_bulk(self, val: str) -> bytes: b = val.encode(); return f"${len(b)}\r\n{val}\r\n".encode()
def set_nx(self, k: str, v: str) -> bool:
if k in self._dict: return False
self._dict[k] = v; return True
r = InMemoryRespSim()
print("RESP:", r.encode_bulk("PONG"), "| FirstNX:", r.set_nx("boot", "1"), "| RetryNX:", r.set_nx("boot", "2"))💡 Self-Explanatory Overview: In simple, everyday terms, Redis vs RDBMS — Polyglot Persistence, Cache-Aside vs Write-Through & Stampede / Avalanche Defense teaches you how Redis applications why Redis complements (rather than blindly replaces) PostgreSQL/MySQL: ACID system-of-record disk durability + ad-hoc SQL joins in RDBMS vs ephemeral/semi-persistent `O(1)` hot reads, counters, leaderboards, and locks in Redis; plus Cache-Aside (` Read: Cache -> DB -> Set Cache; Write: Update DB -> Delete Cache!`) and TTL jitter. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis vs RDBMS — Polyglot Persistence, Cache-Aside vs Write-Through & Stampede / Avalanche Defense in real Redis systems — specifically: Why Redis complements (rather than blindly replaces) PostgreSQL/MySQL: ACID system-of-record disk durability + ad-hoc SQL joins in RDBMS vs ephemeral/semi-persistent `O(1)` hot reads, counters, leaderboards, and locks in Redis; plus Cache-Aside (` Read: Cache -> DB -> Set Cache; Write: Update DB -> Delete Cache!`) and TTL jitter, so your code stays reliable, fast, and easy to maintain in production.
Following is a list of differences between Redis and RDBMS. RedisRDBMS Redis stores everything in primary memory.RDBMS stores everything in secondary memory. In Redis, Read and Write operations are extremely fast because of storing data in primary memory.In RDBMS, Read and Write operations are slow because of storing data in secondary memory. Primary memory is in lesser in size and much expensive than secondary so, R...
Redis vs RDBMS — Polyglot Persistence, Cache-Aside vs Write-Through & Stampede / Avalanche Defense — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis vs RDBMS — Polyglot Persistence, Cache-Aside vs Write-Through & Stampede / Avalanche Defe...
Redis vs RDBMS — Polyglot Persistence, Cache-Aside vs Write-Through & Stampede / Avalanche Defense — Hands-On Lab Task
def build_cache_plan(row: dict | None, base_ttl: int, jitter_offset: int) -> tuple[str, int]:
if row is None:
return ("__NULL_SENTINEL__", 30)
import json
payload = json.dumps(row, separators=(",", ":"), sort_keys=True)
return (payload, base_ttl + (jitter_offset % 60))
hit_val, hit_ttl = build_cache_plan({"id": 101, "fee": 999}, 600, 37)
miss_val, miss_ttl = build_cache_plan(None, 600, 37)
print(f"Hit=({hit_val}, ttl={hit_ttl}s) | Miss=({miss_val}, ttl={miss_ttl}s)")Implement a Python `redis-py` Cache-Aside wrapper protecting PostgreSQL against both missing-ID Cache Penetration attacks and synchronized TTL-expiry Cache Avalanches.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Installing Redis 7 in Production — `vm.overcommit_memory=1`, Disabling `THP` & `systemd` Supervision teaches you how Redis applications installing official `packages.redis.io` / Alpine OCI images (`redis:7.4-alpine`), `supervised systemd`, and why Redis logs startup warnings if Linux `vm.overcommit_memory` is `0` (`fork()` fails with `Cannot allocate memory` even when 40% RAM is free!) or Transparent Huge Pages (`THP`) are enabled!. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Installing Redis 7 in Production — `vm.overcommit_memory=1`, Disabling `THP` & `systemd` Supervision in real Redis systems — specifically: Installing official `packages.redis.io` / Alpine OCI images (`redis:7.4-alpine`), `supervised systemd`, and why Redis logs startup warnings if Linux `vm.overcommit_memory` is `0` (`fork()` fails with `Cannot allocate memory` even when 40% RAM is free!) or Transparent Huge Pages (`THP`) are enabled!, so your code stays reliable, fast, and easy to maintain in production.
Go to Redis official website https://redis.io/ and follow images given below: Scroll down to Windows section. You will see learn more. Click on it. You will get release page: Click on download: You can see that Redis is downloaded now. You can also download one click Redis and install as a Windows service by using the following Github link. https://github.com/rgl/redis/downloads Start Redis Redis is now ready to use....
Installing Redis 7 in Production — `vm.overcommit_memory=1`, Disabling `THP` & `systemd` Supervision — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Installing Redis 7 in Production — `vm.overcommit_memory=1`, Disabling `THP` & `systemd` Superv...
Installing Redis 7 in Production — `vm.overcommit_memory=1`, Disabling `THP` & `systemd` Supervision — Hands-On Lab Task
def audit_redis_host(ram_gb: int, maxmemory_gb: int, overcommit: int, thp: str) -> list[str]:
issues: list[str] = []
if overcommit != 1: issues.append("SET_VM_OVERCOMMIT_1")
if thp != "never": issues.append("DISABLE_THP_2MB_COW")
if maxmemory_gb > ram_gb * 0.75: issues.append("INSUFFICIENT_FORK_HEADROOM")
return issues or ["HOST_PRODUCTION_READY"]
print("ProdHost:", audit_redis_host(32, 24, 1, "never"))
print("BadHost :", audit_redis_host(32, 30, 0, "always"))💡 Self-Explanatory Overview: In simple, everyday terms, Redis Configuration (`redis.conf`) — `maxmemory-policy` (`LFU` vs `LRU`), `lazyfree` & `CONFIG REWRITE` teaches you how Redis applications navigating `redis.conf`: network `bind 127.0.0.1 -::1` & `protected-mode yes`, `tcp-keepalive 300`, `maxmemory` & the 8 `maxmemory-policy` modes (`noeviction`, `allkeys-lru`, `allkeys-lfu`, `volatile-lru`, `volatile-lfu`, `volatile-ttl`), `lazyfree-lazy-eviction yes` non-blocking background thread eviction, and live `CONFIG SET` + `CONFIG REWRITE`. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Configuration (`redis.conf`) — `maxmemory-policy` (`LFU` vs `LRU`), `lazyfree` & `CONFIG REWRITE` in real Redis systems — specifically: Navigating `redis.conf`: network `bind 127.0.0.1 -::1` & `protected-mode yes`, `tcp-keepalive 300`, `maxmemory` & the 8 `maxmemory-policy` modes (`noeviction`, `allkeys-lru`, `allkeys-lfu`, `volatile-lru`, `volatile-lfu`, `volatile-ttl`), `lazyfree-lazy-eviction yes` non-blocking background thread eviction, and live `CONFIG SET` + `CONFIG REWRITE`, so your code stays reliable, fast, and easy to maintain in production.
In Redis, there is a configuration file (redis.conf) available at the root directory of Redis. Although you can get and set all Redis configurations by Redis CONFIG command. Syntax It has the following Syntax. Following is the basic syntax of Redis CONFIG command. redis 127.0.0.1:6379> CONFIG GET CONFIG_SETTING_NAME Example redis 127.0.0.1:6379> CONFIG GET loglevel "loglevel" "notice" To get...
Redis Configuration (`redis.conf`) — `maxmemory-policy` (`LFU` vs `LRU`), `lazyfree` & `CONFIG REWRITE` — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis Configuration (`redis.conf`) — `maxmemory-policy` (`LFU` vs `LRU`), `lazyfree` & `CONFIG...
Redis Configuration (`redis.conf`) — `maxmemory-policy` (`LFU` vs `LRU`), `lazyfree` & `CONFIG REWRITE` — Hands-On Lab Task
def recommend_redis_conf(workload: str, node_ram_mb: int) -> dict[str, str]:
policy_map = {
"pure_cache": "allkeys-lfu",
"mixed_cache_and_permanent": "volatile-lfu",
"celery_broker_or_locks": "noeviction",
}
return {
"maxmemory": f"{int(node_ram_mb * 0.75)}mb",
"maxmemory-policy": policy_map[workload],
"lazyfree-lazy-eviction": "yes",
}
print(recommend_redis_conf("pure_cache", 16384))Author a production `redis.conf` configuration validator ensuring `bind`, `protected-mode`, `maxmemory` CoW headroom, `allkeys-lfu` cache eviction, and disabled опасные commands (`FLUSHALL` / `KEYS`).
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Redis Data Types Overview — Core 5 + `HyperLogLog` (`12 KB`), Bitmaps, Geospatial & Streams teaches you how Redis applications choosing the right data structure: Strings (`SDS` binary-safe <=`512 MB`), Hashes (field-value objects), Lists (`quicklist` queues), Sets (unique membership/intersections), Sorted Sets (`skiplist` leaderboards), **Bitmaps** (`1 bit` per user daily active flag!), **HyperLogLog** (`PFADD`/`PFCOUNT` counting billions of unique items in **`12 KB`**!), **Geo** (`GEOADD`/`GEOSEARCH`), and **Streams** (`XADD`/`XREADGROUP`). Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Data Types Overview — Core 5 + `HyperLogLog` (`12 KB`), Bitmaps, Geospatial & Streams in real Redis systems — specifically: Choosing the right data structure: Strings (`SDS` binary-safe <=`512 MB`), Hashes (field-value objects), Lists (`quicklist` queues), Sets (unique membership/intersections), Sorted Sets (`skiplist` leaderboards), **Bitmaps** (`1 bit` per user daily active flag!), **HyperLogLog** (`PFADD`/`PFCOUNT` counting billions of unique items in **`12 KB`**!), **Geo** (`GEOADD`/`GEOSEARCH`), and **Streams** (`XADD`/`XREADGROUP`), so your code stays reliable, fast, and easy to maintain in production.
There are five types of data types supported by Redis database. Strings Hashes Lists Sets Sorted Sets Strings String is a set of bytes. In Redis database, strings are binary safe. It means they have a known length and not determined by any special terminating characters. So it is possible to store anything up to 512 megabytes in one string. Example Let's store a string name "Ajeet Kumar" in the key by...
Redis Data Types Overview — Core 5 + `HyperLogLog` (`12 KB`), Bitmaps, Geospatial & Streams — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis Data Types Overview — Core 5 + `HyperLogLog` (`12 KB`), Bitmaps, Geospatial & Streams usi...
Redis Data Types Overview — Core 5 + `HyperLogLog` (`12 KB`), Bitmaps, Geospatial & Streams — Hands-On Lab Task
def inspect_string_encoding(val: str) -> str:
if val.lstrip("-").isdigit() and -(2**63) <= int(val) < 2**63:
return "int"
return "embstr" if len(val.encode("utf-8")) <= 44 else "raw"
set_mb = round((10_000_000 * 64) / (1024 * 1024), 1)
hll_kb = 12
print("Encodings:", inspect_string_encoding("999"), inspect_string_encoding("short-jwt"), inspect_string_encoding("x" * 45))
print(f"10M_UVs: Set={set_mb}MB vs HyperLogLog={hll_kb}KB")💡 Self-Explanatory Overview: In simple, everyday terms, Redis Key Management — Colon Namespaces, Cursor `SCAN` (Never `KEYS *`!), `UNLINK` vs `DEL` & `TTL` teaches you how Redis applications designing clean `app:domain:id` key schemas, inspecting existence & expiry (`EXISTS`, `TTL` returning `-2` missing / `-1` no expiry / `>=0` seconds, `PTTL`, `EXPIRE`, `PERSIST`), why `KEYS *` causes production outages (`O(N)` full-keyspace stop-the-world scan), `SCAN cursor MATCH pattern COUNT 500`, and `UNLINK` background thread reclamation. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Key Management — Colon Namespaces, Cursor `SCAN` (Never `KEYS *`!), `UNLINK` vs `DEL` & `TTL` in real Redis systems — specifically: Designing clean `app:domain:id` key schemas, inspecting existence & expiry (`EXISTS`, `TTL` returning `-2` missing / `-1` no expiry / `>=0` seconds, `PTTL`, `EXPIRE`, `PERSIST`), why `KEYS *` causes production outages (`O(N)` full-keyspace stop-the-world scan), `SCAN cursor MATCH pattern COUNT 500`, and `UNLINK` background thread reclamation, so your code stays reliable, fast, and easy to maintain in production.
Redis Keys are used with Redis command to manage themselves. See the syntax of using Redis keys with commands. Syntax It has the following Syntax. redis 127.0.0.1:6379> COMMAND KEY_NAME Example Let's take an example of Redis key using with Redis DEL command. It will give an output 1, if the key is deleted otherwise it will be 0. redis 127.0.0.1:6379> SET MockAttempt Academy redis OK redis 127.0.0.1:6379>...
Redis Key Management — Colon Namespaces, Cursor `SCAN` (Never `KEYS *`!), `UNLINK` vs `DEL` & `TTL` — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis Key Management — Colon Namespaces, Cursor `SCAN` (Never `KEYS *`!), `UNLINK` vs `DEL` & `...
Redis Key Management — Colon Namespaces, Cursor `SCAN` (Never `KEYS *`!), `UNLINK` vs `DEL` & `TTL` — Hands-On Lab Task
def batched_scan_unlink(all_keys: list[str], prefix: str, batch_size: int = 3) -> tuple[int, int]:
matched = [k for k in all_keys if k.startswith(prefix)]
batches = 0; unlinked = 0
for i in range(0, len(matched), batch_size):
chunk = matched[i:i + batch_size]
unlinked += len(chunk); batches += 1
return (unlinked, batches)
keys = [f"sess:u{i}" for i in range(7)] + ["cfg:global"]
print("Unlinked,Batches =", batched_scan_unlink(keys, "sess:", batch_size=3))Build an administrative cache-invalidation tool in Python that purges `tenant:42:*` keys across a 20-million-key Redis instance without ever running `KEYS` or blocking the main event loop.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Redis Strings (`SDS`) — `SET NX PX` Atomic Locks, `INCRBY` Counters, `MGET` & `GETDEL` teaches you how Redis applications why Redis uses C **Simple Dynamic Strings (`SDS`)** instead of null-terminated `char*` (`O(1)` `STRLEN`, binary safety for MessagePack/Protobuf bytes up to `512 MB`, buffer-overflow immunity), atomic `SET key token NX PX 5000` locks/idempotency keys, `GETEX`/`GETDEL` one-time OTP verification, and `INCR`/`INCRBY`. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Strings (`SDS`) — `SET NX PX` Atomic Locks, `INCRBY` Counters, `MGET` & `GETDEL` in real Redis systems — specifically: Why Redis uses C **Simple Dynamic Strings (`SDS`)** instead of null-terminated `char*` (`O(1)` `STRLEN`, binary safety for MessagePack/Protobuf bytes up to `512 MB`, buffer-overflow immunity), atomic `SET key token NX PX 5000` locks/idempotency keys, `GETEX`/`GETDEL` one-time OTP verification, and `INCR`/`INCRBY`, so your code stays reliable, fast, and easy to maintain in production.
Redis strings commands are used to manage string values in Redis. See the syntax for using Redis string commands. Syntax It has the following Syntax. redis 127.0.0.1:6379> COMMAND KEY_NAME Example redis 127.0.0.1:6379> SET MockAttempt Academy redis OK redis 127.0.0.1:6379> GET MockAttempt Academy "redis" Here, SET and GET are commands and "MockAttempt Academy" is key. Redis Strings Comman...
Redis Strings (`SDS`) — `SET NX PX` Atomic Locks, `INCRBY` Counters, `MGET` & `GETDEL` — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis Strings (`SDS`) — `SET NX PX` Atomic Locks, `INCRBY` Counters, `MGET` & `GETDEL` using SE...
Redis Strings (`SDS`) — `SET NX PX` Atomic Locks, `INCRBY` Counters, `MGET` & `GETDEL` — Hands-On Lab Task
class AtomicStringStore:
def __init__(self) -> None: self.data: dict[str, tuple[str, int]] = {}
def set(self, k: str, v: str, nx: bool = False, ex: int = 0) -> bool:
if nx and k in self.data: return False
self.data[k] = (v, ex); return True
def getdel(self, k: str) -> str | None:
item = self.data.pop(k, None)
return item[0] if item else None
s = AtomicStringStore()
s.set("otp:u9", "482910", nx=True, ex=300)
print("OTP_1st:", s.getdel("otp:u9"), "| OTP_Replay:", s.getdel("...💡 Self-Explanatory Overview: In simple, everyday terms, Redis Hashes — Compact `listpack` Object Modeling, `HSET` / `HMGET` / `HINCRBY` & Avoiding `HGETALL` Stalls teaches you how Redis applications storing structured entities (`user:1001 -> {name, plan, credits}`) inside a single Redis Hash instead of 8 separate String keys (slashing `dictEntry` pointer overhead by `5x–8x`!), atomic field mutations (`HINCRBY user:1001 credits -1` without JSON deserialize-modify-serialize races!), and `HMGET` vs `HSCAN`. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Hashes — Compact `listpack` Object Modeling, `HSET` / `HMGET` / `HINCRBY` & Avoiding `HGETALL` Stalls in real Redis systems — specifically: Storing structured entities (`user:1001 -> {name, plan, credits}`) inside a single Redis Hash instead of 8 separate String keys (slashing `dictEntry` pointer overhead by `5x–8x`!), atomic field mutations (`HINCRBY user:1001 credits -1` without JSON deserialize-modify-serialize races!), and `HMGET` vs `HSCAN`, so your code stays reliable, fast, and easy to maintain in production.
Redis Hashes are the perfect data type to represent objects. They used to map between the string fields and the string values. In Redis, every hash can store up to more than 4 billion field-value pairs. Example redis 127.0.0.1:6379> HMSET MockAttempt Academy name "A solution to all Technology" description "India's fastest growing tutorial website" daily 1 million visitors 10 millions page...
Redis Hashes — Compact `listpack` Object Modeling, `HSET` / `HMGET` / `HINCRBY` & Avoiding `HGETALL` Stalls — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis Hashes — Compact `listpack` Object Modeling, `HSET` / `HMGET` / `HINCRBY` & Avoiding `HGE...
Redis Hashes — Compact `listpack` Object Modeling, `HSET` / `HMGET` / `HINCRBY` & Avoiding `HGETALL` Stalls — Hands-On Lab Task
class HashStoreSim:
def __init__(self) -> None: self.h: dict[str, dict[str, str]] = {}
def hset(self, k: str, mapping: dict[str, str]) -> int:
bucket = self.h.setdefault(k, {}); added = sum(1 for f in mapping if f not in bucket)
bucket.update(mapping); return added
def hincrby(self, k: str, f: str, amt: int) -> int:
val = int(self.h.setdefault(k, {}).get(f, "0")) + amt; self.h[k][f] = str(val); return val
def hmget(self, k: str, fields: list[str]) -> list[str | None]: return...Build an atomic payment-webhook deduplicator using single-command `SET ... NX EX` plus a memory-dense user cart/profile store using `listpack`-optimized Redis Hashes.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Redis Lists (`quicklist`) — `LPUSH`/`RPOP`, Capped `LTRIM` Feeds & Crash-Safe `BLMOVE` Queues teaches you how Redis applications how Redis implements Lists as a **`quicklist`** (a doubly-linked list of compact `listpack` chunks — giving `O(1)` head/tail push-pop WITHOUT per-element `prev/next` pointer bloat!), `BLPOP` blocking waits (`0` CPU busy-polling!), why plain `BRPOP` loses the message if the worker dies mid-task, and atomic `BLMOVE` reliable queues. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Lists (`quicklist`) — `LPUSH`/`RPOP`, Capped `LTRIM` Feeds & Crash-Safe `BLMOVE` Queues in real Redis systems — specifically: How Redis implements Lists as a **`quicklist`** (a doubly-linked list of compact `listpack` chunks — giving `O(1)` head/tail push-pop WITHOUT per-element `prev/next` pointer bloat!), `BLPOP` blocking waits (`0` CPU busy-polling!), why plain `BRPOP` loses the message if the worker dies mid-task, and atomic `BLMOVE` reliable queues, so your code stays reliable, fast, and easy to maintain in production.
Redis lists can be defined as a list of strings, sorted by insertion order. You can add elements in Redis lists in the head or the tail of the list. A list can contain more than 4 billion elements. Example redis 127.0.0.1:6379> LPUSH MockAttempt Academy sql (integer) 1 redis 127.0.0.1:6379> LPUSH MockAttempt Academy mysql (integer) 2 redis 127.0.0.1:6379> LPUSH MockAttempt Academy cassandra (integer) 3 redis...
Redis Lists (`quicklist`) — `LPUSH`/`RPOP`, Capped `LTRIM` Feeds & Crash-Safe `BLMOVE` Queues — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis Lists (`quicklist`) — `LPUSH`/`RPOP`, Capped `LTRIM` Feeds & Crash-Safe `BLMOVE` Queues u...
Redis Lists (`quicklist`) — `LPUSH`/`RPOP`, Capped `LTRIM` Feeds & Crash-Safe `BLMOVE` Queues — Hands-On Lab Task
from collections import deque
pending = deque(["job-3", "job-2", "job-1"]) # RPOP pops job-1 (FIFO)
inflight: deque[str] = deque()
claimed = pending.pop(); inflight.appendleft(claimed) # LMOVE pending inflight RIGHT LEFT
feed: list[str] = []
for ev in ["e1", "e2", "e3", "e4"]:
feed.insert(0, ev); feed = feed[:3] # LPUSH + LTRIM 0 2
print("Claimed:", claimed, "| Inflight:", list(inflight), "| CappedFeed:", feed)💡 Self-Explanatory Overview: In simple, everyday terms, Redis Sets — `O(1)` `SISMEMBER`, `SMISMEMBER` Batch Checks, `intset` Encoding & Set Algebra (`SINTER`) teaches you how Redis applications unordered collections of unique strings: `O(1)` membership (`SISMEMBER`) and Redis 6.2+ batch `SMISMEMBER` (checking 50 liked post IDs in 1 command!), `SPOP` (random removal, e.g. raffle/prize pool) vs `SRANDMEMBER` (non-destructive sample), `SINTER` / `SUNION` / `SDIFF`, and compact sorted **`intset`** encoding for integer Sets (`<=512` ints). Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Sets — `O(1)` `SISMEMBER`, `SMISMEMBER` Batch Checks, `intset` Encoding & Set Algebra (`SINTER`) in real Redis systems — specifically: Unordered collections of unique strings: `O(1)` membership (`SISMEMBER`) and Redis 6.2+ batch `SMISMEMBER` (checking 50 liked post IDs in 1 command!), `SPOP` (random removal, e.g. raffle/prize pool) vs `SRANDMEMBER` (non-destructive sample), `SINTER` / `SUNION` / `SDIFF`, and compact sorted **`intset`** encoding for integer Sets (`<=512` ints), so your code stays reliable, fast, and easy to maintain in production.
Redis Sets are an unordered collection of unique strings. Unique strings means there is not a single string repeated in the set. In Redis set add, remove, and test for the existence of members in O(1) (constant time regardless of the number of elements contained inside the Set). The maximum length of a list is more than 4 billion of elements per set. Example redis 127.0.0.1:6379> SADD MockAttempt Academy db2 (inte...
Redis Sets — `O(1)` `SISMEMBER`, `SMISMEMBER` Batch Checks, `intset` Encoding & Set Algebra (`SINTER`) — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis Sets — `O(1)` `SISMEMBER`, `SMISMEMBER` Batch Checks, `intset` Encoding & Set Algebra (`S...
Redis Sets — `O(1)` `SISMEMBER`, `SMISMEMBER` Batch Checks, `intset` Encoding & Set Algebra (`SINTER`) — Hands-On Lab Task
sets = {
"skill:kafka": {101, 102, 105, 109},
"skill:k8s": {102, 105, 108, 109, 112},
}
def smismember(key: str, candidates: list[int]) -> list[int]:
s = sets.get(key, set()); return [1 if c in s else 0 for c in candidates]
inter_card = len(sets["skill:kafka"] & sets["skill:k8s"])
print("SMISMEMBER:", smismember("skill:kafka", [101, 999, 105]), "| SINTERCARD:", inter_card)Implement a reliable queue pattern where crashed workers never drop in-flight jobs (`LMOVE` into a `processing` list until acked via `LREM`), plus a real-time Set intersection skill-matching filter.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Redis Sorted Sets (`ZSET`) — Dual `Dict + SkipList` Internals, Leaderboards & Sliding-Window Rate Limiting teaches you how Redis applications redis’s crown-jewel structure: every large `ZSET` maintains BOTH a **Hash Table** (`member -> score` in `O(1)`) AND a probabilistic multi-level **SkipList** (sorted by `IEEE-754 double score`, breaking score ties lexicographically by member bytes in `O(log N)`!), powering live leaderboards, priority delay queues (`ZMPOP`), and sliding-window rate limiters. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Sorted Sets (`ZSET`) — Dual `Dict + SkipList` Internals, Leaderboards & Sliding-Window Rate Limiting in real Redis systems — specifically: Redis’s crown-jewel structure: every large `ZSET` maintains BOTH a **Hash Table** (`member -> score` in `O(1)`) AND a probabilistic multi-level **SkipList** (sorted by `IEEE-754 double score`, breaking score ties lexicographically by member bytes in `O(log N)`!), powering live leaderboards, priority delay queues (`ZMPOP`), and sliding-window rate limiters, so your code stays reliable, fast, and easy to maintain in production.
Redis Sorted Sets are similar to Redis Sets but the first one has the unique feature of values stored. It means every member of a Sorted Set is associated with a score which can be used to take the sorted set ordered, from the smallest to the greatest score. Example redis 127.0.0.1:6379> ZADD MockAttempt Academy 1 redis (integer) 0 redis 127.0.0.1:6379> ZADD MockAttempt Academy 2 cassandra (integer) 1 redis 127...
Redis Sorted Sets (`ZSET`) — Dual `Dict + SkipList` Internals, Leaderboards & Sliding-Window Rate Limiting — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis Sorted Sets (`ZSET`) — Dual `Dict + SkipList` Internals, Leaderboards & Sliding-Window Ra...
Redis Sorted Sets (`ZSET`) — Dual `Dict + SkipList` Internals, Leaderboards & Sliding-Window Rate Limiting — Hands-On Lab Task
class LeaderboardZSet:
def __init__(self) -> None: self.scores: dict[str, float] = {}
def zadd_gt(self, member: str, score: float) -> bool:
if member not in self.scores or score > self.scores[member]:
self.scores[member] = score; return True
return False
def top_n(self, n: int) -> list[tuple[str, float]]:
return sorted(self.scores.items(), key=lambda kv: (-kv[1], kv[0]))[:n]
lb = LeaderboardZSet(); lb.zadd_gt("aarav", 92.0); lb.zadd_gt("diya", 96.5); lb.zadd_gt("aarav...💡 Self-Explanatory Overview: In simple, everyday terms, Redis `HSET` & Hash Expiration — Key-Level `EXPIRE` vs Redis 7.4 Native Per-Field `HEXPIRE` & `HTTL` teaches you how Redis applications solving a decade-long Redis interview classic! Prior to Redis 7.4, `EXPIRE` worked ONLY on top-level keys (not individual Hash fields) — AND note that `HSET` preserves existing key TTL (unlike `SET` without `KEEPTTL`, which wipes TTL!). In **Redis 7.4+**, native Hash Field Expiration (`HEXPIRE`, `HPEXPIRE`, `HEXPIREAT`, `HTTL`, `HPERSIST`) expires individual fields inside a Hash independently!. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis `HSET` & Hash Expiration — Key-Level `EXPIRE` vs Redis 7.4 Native Per-Field `HEXPIRE` & `HTTL` in real Redis systems — specifically: Solving a decade-long Redis interview classic! Prior to Redis 7.4, `EXPIRE` worked ONLY on top-level keys (not individual Hash fields) — AND note that `HSET` preserves existing key TTL (unlike `SET` without `KEEPTTL`, which wipes TTL!). In **Redis 7.4+**, native Hash Field Expiration (`HEXPIRE`, `HPEXPIRE`, `HEXPIREAT`, `HTTL`, `HPERSIST`) expires individual fields inside a Hash independently!, so your code stays reliable, fast, and easy to maintain in production.
Introduction Redis is a popular stored-in-memory information store that may be used as a communication broker, database, and store. HSET, one of its many useful commands, is essential for handling hash data types since it lets you set field-value pairs inside of a hash. However, expiry is not supported by HSET alone. You must use the EXPIRE command in conjunction with a hash to set its expiration time. Once the hash...
Redis `HSET` & Hash Expiration — Key-Level `EXPIRE` vs Redis 7.4 Native Per-Field `HEXPIRE` & `HTTL` — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis `HSET` & Hash Expiration — Key-Level `EXPIRE` vs Redis 7.4 Native Per-Field `HEXPIRE` & `...
Redis `HSET` & Hash Expiration — Key-Level `EXPIRE` vs Redis 7.4 Native Per-Field `HEXPIRE` & `HTTL` — Hands-On Lab Task
class Redis74HashFieldExpiry:
def __init__(self) -> None: self.fields: dict[str, tuple[str, int]] = {}
def hset(self, field: str, val: str) -> None:
old_ttl = self.fields[field][1] if field in self.fields else -1
self.fields[field] = (val, old_ttl) # HSET preserves existing TTL
def hexpire_nx(self, field: str, sec: int) -> int:
if field not in self.fields: return -2
val, cur_ttl = self.fields[field]
if cur_ttl == -1: self.fields[field] = (val, sec); return 1...Build an `O(log N)` MockAttempt live exam leaderboard supporting instant rank + top-10 queries with timestamp tie-breaking, alongside a multi-device user session Hash expiring individual refresh tokens via Redis 7.4 `HEXPIRE`.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Redis Transactions — `MULTI` / `EXEC` / `DISCARD`, Why There Is NO Rollback & `WATCH` Optimistic CAS teaches you how Redis applications how `MULTI` queues commands (`QUEUED`) and `EXEC` runs the entire batch sequentially without interleaving any other client’s commands; the #1 Redis interview gotcha (**Redis does NOT roll back already-executed commands if a later command inside `EXEC` fails at runtime, e.g., `INCR` on a List!**); and Check-And-Set (`CAS`) via `WATCH`. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Transactions — `MULTI` / `EXEC` / `DISCARD`, Why There Is NO Rollback & `WATCH` Optimistic CAS in real Redis systems — specifically: How `MULTI` queues commands (`QUEUED`) and `EXEC` runs the entire batch sequentially without interleaving any other client’s commands; the #1 Redis interview gotcha (**Redis does NOT roll back already-executed commands if a later command inside `EXEC` fails at runtime, e.g., `INCR` on a List!**); and Check-And-Set (`CAS`) via `WATCH`, so your code stays reliable, fast, and easy to maintain in production.
Redis transaction is used to facilitates users to execute group of commands in a single step. There are two properties of execution: All commands in a transaction are sequentially executed as a single isolated operation. You can't issue a request by another client served in the middle of the execution of a Redis transaction. Redis transaction is also atomic. Atomic means either all of the commands or none are pr...
Redis Transactions — `MULTI` / `EXEC` / `DISCARD`, Why There Is NO Rollback & `WATCH` Optimistic CAS — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis Transactions — `MULTI` / `EXEC` / `DISCARD`, Why There Is NO Rollback & `WATCH` Optimisti...
Redis Transactions — `MULTI` / `EXEC` / `DISCARD`, Why There Is NO Rollback & `WATCH` Optimistic CAS — Hands-On Lab Task
def optimistic_debit(balance_at_watch: int, concurrent_edit: bool, debit: int) -> str:
if balance_at_watch < debit:
return "INSUFFICIENT_FUNDS"
if concurrent_edit:
return "WATCH_CONFLICT_ABORTED_EXEC_NIL"
return f"COMMITTED_NEW_BAL={balance_at_watch - debit}"
print("CleanRun :", optimistic_debit(1500, concurrent_edit=False, debit=999))
print("RaceFound:", optimistic_debit(1500, concurrent_edit=True, debit=999))💡 Self-Explanatory Overview: In simple, everyday terms, Redis Lua Scripting & Functions — Atomic `EVALSHA`, Safe Lock Release & `KEYS[]` vs `ARGV[]` Rule teaches you how Redis applications why embedded Lua 5.1 (`EVAL`, `EVALSHA`, and Redis 7 persistent `FUNCTION LOAD` / `FCALL`) beats `WATCH/MULTI/EXEC`: zero network round-trips mid-transaction, zero `WatchError` retry storms, true conditional branching (`if/else` on `redis.call` results!), powering Redlock/lock-release and Token-Bucket rate limiters. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Lua Scripting & Functions — Atomic `EVALSHA`, Safe Lock Release & `KEYS[]` vs `ARGV[]` Rule in real Redis systems — specifically: Why embedded Lua 5.1 (`EVAL`, `EVALSHA`, and Redis 7 persistent `FUNCTION LOAD` / `FCALL`) beats `WATCH/MULTI/EXEC`: zero network round-trips mid-transaction, zero `WatchError` retry storms, true conditional branching (`if/else` on `redis.call` results!), powering Redlock/lock-release and Token-Bucket rate limiters, so your code stays reliable, fast, and easy to maintain in production.
Redis scripting is used to evaluate scripts using the Lua interpreter. It is built into Redis starting from version 2.6.0. The command used for scripting is EVAL command. See the syntax of EVAL command. Syntax It has the following Syntax. redis 127.0.0.1:6379> EVAL script numkeys key [key ...] arg [arg ...] Let's take an example to see how Redis scripting works: redis 127.0.0.1:6379> EVAL "return {KEY...
Redis Lua Scripting & Functions — Atomic `EVALSHA`, Safe Lock Release & `KEYS[]` vs `ARGV[]` Rule — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis Lua Scripting & Functions — Atomic `EVALSHA`, Safe Lock Release & `KEYS[]` vs `ARGV[]` Ru...
Redis Lua Scripting & Functions — Atomic `EVALSHA`, Safe Lock Release & `KEYS[]` vs `ARGV[]` Rule — Hands-On Lab Task
UNLOCK_LUA = """
if redis.call("GET", KEYS[1]) == ARGV[1] then
return redis.call("DEL", KEYS[1])
else
return 0
end
""".strip()
def run_unlock_sim(store: dict[str, str], key: str, caller_token: str) -> int:
if store.get(key) == caller_token: del store[key]; return 1
return 0
locks = {"lock:order:9": "worker-A-uuid"}
print("WrongOwner:", run_unlock_sim(locks, "lock:order:9", "worker-B-uuid"), "| TrueOwner:", run_unlock_sim(locks, "lock:order:9", "worker-A-uuid"))Build two mission-critical Lua scripts registered via `redis-py`: (1) an owner-verified distributed lock release (`if GET == token then DEL`) and (2) a race-free Token-Bucket API rate limiter.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Redis Pipelining — Defeating Network `RTT` Latency, `transaction=False` & Bounded Batch Sizes teaches you how Redis applications why Redis throughput in microservices is almost always bottlenecked by **network Round-Trip Time (`RTT` ~`0.5 ms`)** + `read()/write()` syscall context switches rather than Redis CPU (`~1 us`!), how Pipelining streams `K` commands in `1` TCP write and reads `K` replies in `1` read (`20x` speedup!), and why Pipelining != Atomic Transaction. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Pipelining — Defeating Network `RTT` Latency, `transaction=False` & Bounded Batch Sizes in real Redis systems — specifically: Why Redis throughput in microservices is almost always bottlenecked by **network Round-Trip Time (`RTT` ~`0.5 ms`)** + `read()/write()` syscall context switches rather than Redis CPU (`~1 us`!), how Pipelining streams `K` commands in `1` TCP write and reads `K` replies in `1` read (`20x` speedup!), and why Pipelining != Atomic Transaction, so your code stays reliable, fast, and easy to maintain in production.
Before knowing pipelining, first know the concept of Redis: Redis is a TCP server which supports request/response protocol. In Redis, a request is completed in two steps: The client sends a query to the server usually in a blocking way for the server response. The server processes the command and sends the response back to the client. What is Pipelining Pipelining facilitates a client to send multiple requests to the...
Redis Pipelining — Defeating Network `RTT` Latency, `transaction=False` & Bounded Batch Sizes — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis Pipelining — Defeating Network `RTT` Latency, `transaction=False` & Bounded Batch Sizes u...
Redis Pipelining — Defeating Network `RTT` Latency, `transaction=False` & Bounded Batch Sizes — Hands-On Lab Task
def estimate_wall_ms(total_cmds: int, rtt_ms: float, chunk_size: int | None) -> float:
exec_us_per_cmd = 0.002 # 2 microseconds Redis RAM execution
if chunk_size is None:
return round(total_cmds * (rtt_ms + exec_us_per_cmd), 1)
flushes = (total_cmds + chunk_size - 1) // chunk_size
return round(flushes * rtt_ms + total_cmds * exec_us_per_cmd, 1)
print("NoPipeline (2000 cmds):", estimate_wall_ms(2000, 0.5, None), "ms")
print("Pipelined (chunk=500):", estimate_wall_ms(2000, 0.5, 500), "ms")💡 Self-Explanatory Overview: In simple, everyday terms, Redis Connections & Pooling — `ConnectionPool`, Timeouts, `CLIENT LIST` & Async `redis.asyncio` teaches you how Redis applications why opening a brand-new TCP + TLS + `AUTH` handshake per HTTP request kills latency and exhausts ephemeral ports (`maxclients` default `10,000`), sharing a thread-safe `redis.ConnectionPool` (or `redis.asyncio` in FastAPI/uvloop), `CLIENT SETNAME` observability in `CLIENT LIST`, RESP3 client-side caching, and socket timeout discipline. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Connections & Pooling — `ConnectionPool`, Timeouts, `CLIENT LIST` & Async `redis.asyncio` in real Redis systems — specifically: Why opening a brand-new TCP + TLS + `AUTH` handshake per HTTP request kills latency and exhausts ephemeral ports (`maxclients` default `10,000`), sharing a thread-safe `redis.ConnectionPool` (or `redis.asyncio` in FastAPI/uvloop), `CLIENT SETNAME` observability in `CLIENT LIST`, RESP3 client-side caching, and socket timeout discipline, so your code stays reliable, fast, and easy to maintain in production.
Redis connection commands are used to control and manage client connections with Redis Server. Example Following example explains how a client authenticates itself to Redis server and checks whether the server is running or not. redis 127.0.0.1:6379> AUTH "password" (error) ERR Client sent AUTH, but no password is set redis 127.0.0.1:6379> redis 127.0.0.1:6379> PING PONG redis 127.0.0.1:6379> Re...
Redis Connections & Pooling — `ConnectionPool`, Timeouts, `CLIENT LIST` & Async `redis.asyncio` — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis Connections & Pooling — `ConnectionPool`, Timeouts, `CLIENT LIST` & Async `redis.asyncio`...
Redis Connections & Pooling — `ConnectionPool`, Timeouts, `CLIENT LIST` & Async `redis.asyncio` — Hands-On Lab Task
import redis
pool = redis.ConnectionPool(
host="redis-primary.internal",
port=6379,
max_connections=64,
decode_responses=True,
socket_connect_timeout=1.0,
socket_timeout=2.0,
health_check_interval=30,
client_name="checkout-api-v2",
)
kw = pool.connection_kwargs
print(f"Client={kw['client_name']} | MaxConn={pool.max_connections} | Timeout={kw['socket_timeout']}s")Build a Python `redis-py` bulk feature-flag hydrator that chunks 25,000 writes into bounded 1,000-command `pipeline(transaction=False)` flushes over a timeout-hardened `ConnectionPool`.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Redis Backup, Restore & Persistence — `BGSAVE` (`RDB`) vs `AOF` (`everysec`) & Hybrid RDB-Preamble teaches you how Redis applications comparing **RDB** (compact point-in-time binary `dump.rdb` created via child `fork()` `BGSAVE` — fast restores & S3 off-site backups, but loses minutes since last snapshot) vs **AOF** (`appendfsync everysec` losing at most `1 second` of writes + `BGREWRITEAOF` compaction), and Redis 7 Multi-Part AOF (`manifest` + `base.rdb` + `incr.aof`). Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Backup, Restore & Persistence — `BGSAVE` (`RDB`) vs `AOF` (`everysec`) & Hybrid RDB-Preamble in real Redis systems — specifically: Comparing **RDB** (compact point-in-time binary `dump.rdb` created via child `fork()` `BGSAVE` — fast restores & S3 off-site backups, but loses minutes since last snapshot) vs **AOF** (`appendfsync everysec` losing at most `1 second` of writes + `BGREWRITEAOF` compaction), and Redis 7 Multi-Part AOF (`manifest` + `base.rdb` + `incr.aof`), so your code stays reliable, fast, and easy to maintain in production.
SAVE command is used to create a backup of a current Redis database. This command will create a dump.rdb file in your Redis directory by performing synchronous save. Syntax It has the following Syntax. The SAVE command returns OK after successful execution. Redis Backup Example Use the SAVE command to create a backup of the current database. It will create a dump.rdb file in your Redis directory. You can see that the...
Redis Backup, Restore & Persistence — `BGSAVE` (`RDB`) vs `AOF` (`everysec`) & Hybrid RDB-Preamble — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis Backup, Restore & Persistence — `BGSAVE` (`RDB`) vs `AOF` (`everysec`) & Hybrid RDB-Pream...
Redis Backup, Restore & Persistence — `BGSAVE` (`RDB`) vs `AOF` (`everysec`) & Hybrid RDB-Preamble — Hands-On Lab Task
def boot_data_source(appendonly: bool, has_aof_files: bool, has_dump_rdb: bool) -> tuple[str, str]:
if appendonly:
src = "LOAD_AOF_HYBRID" if has_aof_files else "EMPTY_AOF_WARNING_IGNORES_RDB"
return (src, "RPO<=1s (everysec)")
if has_dump_rdb:
return ("LOAD_DUMP_RDB", "RPO=Minutes (snapshot)")
return ("EMPTY_MEMORY", "RPO=Volatile")
print("HealthyProd :", boot_data_source(True, True, True))
print("RestoreTrap :", boot_data_source(True, False, True))💡 Self-Explanatory Overview: In simple, everyday terms, Redis 7 Security Hardening — Granular `ACL`s (Key & Command Prefixes), `default off`, TLS 1.3 & VPC Isolation teaches you how Redis applications why exposed unauthenticated `0.0.0.0:6379` Redis instances get cryptomined within minutes (`CONFIG SET dir /root/.ssh` exploit!): network `bind` + `protected-mode yes` + private VPC Security Groups, disabling `user default off`, creating per-service **Redis 6/7 ACLs** (`~prefix:*`, `&channel:*`, `+@read +@write -@dangerous -@admin`), and mTLS encryption in transit. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis 7 Security Hardening — Granular `ACL`s (Key & Command Prefixes), `default off`, TLS 1.3 & VPC Isolation in real Redis systems — specifically: Why exposed unauthenticated `0.0.0.0:6379` Redis instances get cryptomined within minutes (`CONFIG SET dir /root/.ssh` exploit!): network `bind` + `protected-mode yes` + private VPC Security Groups, disabling `user default off`, creating per-service **Redis 6/7 ACLs** (`~prefix:*`, `&channel:*`, `+@read +@write -@dangerous -@admin`), and mTLS encryption in transit, so your code stays reliable, fast, and easy to maintain in production.
Security is very necessary for a database to keep the data safe and secure. It provides an authentication so if a client want to make a connection needs to authenticate before executing a command. You need to set a password in the config file to secure a Redis databse. Example Let's see how to secure your Redis instance. Use the "config get command" config get requirepass You can see that the above pro...
Redis 7 Security Hardening — Granular `ACL`s (Key & Command Prefixes), `default off`, TLS 1.3 & VPC Isolation — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis 7 Security Hardening — Granular `ACL`s (Key & Command Prefixes), `default off`, TLS 1.3 &...
Redis 7 Security Hardening — Granular `ACL`s (Key & Command Prefixes), `default off`, TLS 1.3 & VPC Isolation — Hands-On Lab Task
def build_acl_rule(service: str, secret_hash: str) -> str:
return (
f"ACL SETUSER svc_{service} on #{secret_hash} "
f"~{service}:* &{service}_events:* "
f"+@read +@write +@scripting -@dangerous -@admin"
)
sha256_demo = "8c6976e5b5410415bde908bd4dee15dfb167a9c873fc4bb8a81f6f2ab448a918"
rule = build_acl_rule("payments", sha256_demo)
print(rule[:52] + "... | Scoped:", "~payments:*" in rule and "-@dangerous" in rule)Design a safe Redis persistence & restore runbook avoiding the empty-`appendonly.aof` data-wipe trap, paired with a microservice-scoped Redis 7 `ACL` rule generator.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Redis Benchmarking & Latency Forensics — `redis-benchmark` (`-r`, `-d`, `-P`), `--intrinsic-latency` & Tail p99 teaches you how Redis applications running honest load tests with `redis-benchmark`: why omitting `-r <keyspacelen>` hammers the **same 1 single key** in L1 CPU cache, testing realistic payload bytes (`-d 512`), measuring pipeline scaling (`-P 16`), multi-threaded client generation (`--threads 4` from a **separate** VM so benchmark client CPU doesn’t starve `redis-server`!), and `redis-cli --latency-history`. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Benchmarking & Latency Forensics — `redis-benchmark` (`-r`, `-d`, `-P`), `--intrinsic-latency` & Tail p99 in real Redis systems — specifically: Running honest load tests with `redis-benchmark`: why omitting `-r <keyspacelen>` hammers the **same 1 single key** in L1 CPU cache, testing realistic payload bytes (`-d 512`), measuring pipeline scaling (`-P 16`), multi-threaded client generation (`--threads 4` from a **separate** VM so benchmark client CPU doesn’t starve `redis-server`!), and `redis-cli --latency-history`, so your code stays reliable, fast, and easy to maintain in production.
Redis benchmark is a utility tool which is used to check the performance of Redis by using multiple (n) command simultaneously. Syntax It has the following Syntax. redis-benchmark [option] [option value] Example Let's call Redis Benchmarkcommands. redis-benchmark -n 100000 See the list of available options in Redis benchmark: IndexOptionDescriptionDefault Value 1-hSpecifies server host name127.0.0.1 2-pSpecifies...
Redis Benchmarking & Latency Forensics — `redis-benchmark` (`-r`, `-d`, `-P`), `--intrinsic-latency` & Tail p99 — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis Benchmarking & Latency Forensics — `redis-benchmark` (`-r`, `-d`, `-P`), `--intrinsic-lat...
Redis Benchmarking & Latency Forensics — `redis-benchmark` (`-r`, `-d`, `-P`), `--intrinsic-latency` & Tail p99 — Hands-On Lab Task
def build_benchmark_cmd(host: str, clients: int, requests: int, keyspace: int, data_bytes: int, pipe: int) -> str:
assert host not in ("127.0.0.1", "localhost"), "Run benchmark from separate client VM!"
assert keyspace >= 10_000 and data_bytes >= 128, "Use realistic keyspace & payload size!"
return (
f"redis-benchmark -h {host} -t set,get -c {clients} -n {requests} "
f"-r {keyspace} -d {data_bytes} -P {pipe} --threads 4"
)
print(build_benchmark_cmd("10.20.4.15", 50, 500000, 100000, 5...💡 Self-Explanatory Overview: In simple, everyday terms, Redis Partitioning & Cluster HA — Sentinel vs `16,384` Hash Slots (`CRC16`), `{HashTags}` & `CROSSSLOT` teaches you how Redis applications scaling Redis horizontally: why naive `hash(key) % N` loses `(N-1)/N` of keys when adding a node vs Consistent Hashing vs **Redis Cluster’s `16,384` deterministic Hash Slots** (`slot = CRC16(key) & 16383`), minimum **6-node production topology** (`3 Primaries + 3 Replicas`), smart client `MOVED`/`ASK` slot maps (`RedisCluster`), and `{user:42}` **Hash Tags** for multi-key operations. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Partitioning & Cluster HA — Sentinel vs `16,384` Hash Slots (`CRC16`), `{HashTags}` & `CROSSSLOT` in real Redis systems — specifically: Scaling Redis horizontally: why naive `hash(key) % N` loses `(N-1)/N` of keys when adding a node vs Consistent Hashing vs **Redis Cluster’s `16,384` deterministic Hash Slots** (`slot = CRC16(key) & 16383`), minimum **6-node production topology** (`3 Primaries + 3 Replicas`), smart client `MOVED`/`ASK` slot maps (`RedisCluster`), and `{user:42}` **Hash Tags** for multi-key operations, so your code stays reliable, fast, and easy to maintain in production.
Partitioning is used to split your Redis data into multiple Redis instances so that every instance contain only a subset of your keys. It is generally used for large databases. Types of Partitioning There are two types of partitioning in redis: Range Partitioning Hash Partitioning Range Partitioning Range partitioning is one of the simpliest way to perform partitioning. It is done by mapping ranges of objects into sp...
Redis Partitioning & Cluster HA — Sentinel vs `16,384` Hash Slots (`CRC16`), `{HashTags}` & `CROSSSLOT` — Practical Assignment
Implement a production-ready Redis 7 Caching, Lua Scripting, Streams & Cluster HA solution demonstrating Redis Partitioning & Cluster HA — Sentinel vs `16,384` Hash Slots (`CRC16`), `{HashTags}` & `CR...
Redis Partitioning & Cluster HA — Sentinel vs `16,384` Hash Slots (`CRC16`), `{HashTags}` & `CROSSSLOT` — Hands-On Lab Task
def extract_hash_target(key: str) -> bytes:
s = key.find("{")
if s != -1:
e = key.find("}", s + 1)
if e != -1 and e > s + 1: # non-empty {tag}
return key[s + 1:e].encode("utf-8")
return key.encode("utf-8")
def crc16_xmodem_slot(key: str) -> int:
crc = 0
for byte in extract_hash_target(key):
crc ^= byte << 8
for _ in range(8): crc = ((crc << 1) ^ 0x1021) & 0xFFFF if (crc & 0x8000) else (crc << 1) & 0xFFFF
return crc % 16384
s1, s2 = crc16_xmodem_slo...Implement the exact XMODEM `CRC16(key) % 16384` Redis Cluster hash-slot router in Python supporting `{...}` hash tags, verifying multi-key Lua/`MGET` slot co-location without creating single-shard hot spots.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Cache-Aside, Read/Write-Through, Negative Caching & TTL Jitter teaches you how Redis applications protect SQL/NoSQL databases from both hot-key floods and repeated missing-ID lookups. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Cache-Aside, Read/Write-Through, Negative Caching & TTL Jitter in real Redis systems — specifically: Protect SQL/NoSQL databases from both hot-key floods and repeated missing-ID lookups, so your code stays reliable, fast, and easy to maintain in production.
What Is Cache-Aside, Read/Write-Through, Negative Caching & TTL Jitter & Why Industry Teams Use ItIn Redis Training, Cache-Aside, Read/Write-Through, Negative Caching & TTL Jitter exists for one clear real-world purpose: Protect SQL/NoSQL databases from both hot-key floods and repeated missing-ID lookups. Even if you come from a non-technical background, we start with the everyday real-life problem first...
Hands-On Implementation: Cache-Aside, Read/Write-Through, Negative Caching & TTL Jitter
Build and verify a clean working module demonstrating Cache-Aside, Read/Write-Through, Negative Caching & TTL Jitter (Protect SQL/NoSQL databases from both hot-key floods and repeated missing-ID looku...
Live Lab Sprint: Cache-Aside, Read/Write-Through, Negative Caching & TTL Jitter
-- Cache-Aside, Read/Write-Through, Negative Caching & TTL Jitter
-- Purpose: Protect SQL/NoSQL databases from both hot-key floods and repeated missing-ID lookups.
local current = redis.call('INCR', KEYS[1])
if current == 1 then redis.call('PEXPIRE', KEYS[1], ARGV[1]) end
return current💡 Self-Explanatory Overview: In simple, everyday terms, Preventing Cache Stampedes (Thundering Herd) with Single-Flight Locks & XFetch teaches you how Redis applications recompute expiring popular keys on 1 worker while 5,000 readers still get instant responses. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Preventing Cache Stampedes (Thundering Herd) with Single-Flight Locks & XFetch in real Redis systems — specifically: Recompute expiring popular keys on 1 worker while 5,000 readers still get instant responses, so your code stays reliable, fast, and easy to maintain in production.
What Is Preventing Cache Stampedes (Thundering Herd) with Single-Flight Locks & XFetch & Why Industry Teams Use ItIn Redis Training, Preventing Cache Stampedes (Thundering Herd) with Single-Flight Locks & XFetch exists for one clear real-world purpose: Recompute expiring popular keys on 1 worker while 5,000 readers still get instant responses. Even if you come from a non-technical background, we start wit...
Hands-On Implementation: Preventing Cache Stampedes (Thundering Herd) with Single-Flight Locks & XFetch
Build and verify a clean working module demonstrating Preventing Cache Stampedes (Thundering Herd) with Single-Flight Locks & XFetch (Recompute expiring popular keys on 1 worker while 5,000 readers st...
Live Lab Sprint: Preventing Cache Stampedes (Thundering Herd) with Single-Flight Locks & XFetch
-- Preventing Cache Stampedes (Thundering Herd) with Single-Flight Locks & XFetch
-- Purpose: Recompute expiring popular keys on 1 worker while 5,000 readers still get instant responses.
local current = redis.call('INCR', KEYS[1])
if current == 1 then redis.call('PEXPIRE', KEYS[1], ARGV[1]) end
return current💡 Self-Explanatory Overview: In simple, everyday terms, Command Pipelining, Connection Pooling & Avoiding O(N) KEYS/SMEMBERS teaches you how Redis applications slash network round-trip latency 10x and use cursor SCAN so Redis’s single thread never stalls. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Command Pipelining, Connection Pooling & Avoiding O(N) KEYS/SMEMBERS in real Redis systems — specifically: Slash network round-trip latency 10x and use cursor SCAN so Redis’s single thread never stalls, so your code stays reliable, fast, and easy to maintain in production.
What Is Command Pipelining, Connection Pooling & Avoiding O(N) KEYS/SMEMBERS & Why Industry Teams Use ItIn Redis Training, Command Pipelining, Connection Pooling & Avoiding O(N) KEYS/SMEMBERS exists for one clear real-world purpose: Slash network round-trip latency 10x and use cursor SCAN so Redis’s single thread never stalls. Even if you come from a non-technical background, we start with the everyday re...
Hands-On Implementation: Command Pipelining, Connection Pooling & Avoiding O(N) KEYS/SMEMBERS
Build and verify a clean working module demonstrating Command Pipelining, Connection Pooling & Avoiding O(N) KEYS/SMEMBERS (Slash network round-trip latency 10x and use cursor SCAN so Redis’s single t...
Live Lab Sprint: Command Pipelining, Connection Pooling & Avoiding O(N) KEYS/SMEMBERS
-- Command Pipelining, Connection Pooling & Avoiding O(N) KEYS/SMEMBERS
-- Purpose: Slash network round-trip latency 10x and use cursor SCAN so Redis’s single thread never stalls.
local current = redis.call('INCR', KEYS[1])
if current == 1 then redis.call('PEXPIRE', KEYS[1], ARGV[1]) end
return current💡 Self-Explanatory Overview: In simple, everyday terms, Compact Encodings (ziplist/listpack), maxmemory-policy Eviction & MEMORY USAGE teaches you how Redis applications cut Redis RAM bills by 50% and choose allkeys-lfu vs volatile-lru intentionally. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Compact Encodings (ziplist/listpack), maxmemory-policy Eviction & MEMORY USAGE in real Redis systems — specifically: Cut Redis RAM bills by 50% and choose allkeys-lfu vs volatile-lru intentionally, so your code stays reliable, fast, and easy to maintain in production.
What Is Compact Encodings (ziplist/listpack), maxmemory-policy Eviction & MEMORY USAGE & Why Industry Teams Use ItIn Redis Training, Compact Encodings (ziplist/listpack), maxmemory-policy Eviction & MEMORY USAGE exists for one clear real-world purpose: Cut Redis RAM bills by 50% and choose allkeys-lfu vs volatile-lru intentionally. Even if you come from a non-technical background, we start with the everyd...
Hands-On Implementation: Compact Encodings (ziplist/listpack), maxmemory-policy Eviction & MEMORY USAGE
Build and verify a clean working module demonstrating Compact Encodings (ziplist/listpack), maxmemory-policy Eviction & MEMORY USAGE (Cut Redis RAM bills by 50% and choose allkeys-lfu vs volatile-lru...
Live Lab Sprint: Compact Encodings (ziplist/listpack), maxmemory-policy Eviction & MEMORY USAGE
-- Compact Encodings (ziplist/listpack), maxmemory-policy Eviction & MEMORY USAGE
-- Purpose: Cut Redis RAM bills by 50% and choose allkeys-lfu vs volatile-lru intentionally.
local current = redis.call('INCR', KEYS[1])
if current == 1 then redis.call('PEXPIRE', KEYS[1], ARGV[1]) end
return currentDesign, implement, test, and document 'Sub-2ms Multi-Tier Cache Layer Sustaining 50k RPS' applying every production concept from Module 11 (High-Scale Caching Architecture: Stampede Locks, Pipelining & Memory Tuning) of Redis Training.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Server-Side Atomic EVALSHA Lua Scripts vs Optimistic WATCH/MULTI teaches you how Redis applications execute multi-step check-and-decrement logic atomically in microseconds with zero race windows. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Server-Side Atomic EVALSHA Lua Scripts vs Optimistic WATCH/MULTI in real Redis systems — specifically: Execute multi-step check-and-decrement logic atomically in microseconds with zero race windows, so your code stays reliable, fast, and easy to maintain in production.
What Is Server-Side Atomic EVALSHA Lua Scripts vs Optimistic WATCH/MULTI & Why Industry Teams Use ItIn Redis Training, Server-Side Atomic EVALSHA Lua Scripts vs Optimistic WATCH/MULTI exists for one clear real-world purpose: Execute multi-step check-and-decrement logic atomically in microseconds with zero race windows. Even if you come from a non-technical background, we start with the everyday real-life problem...
Hands-On Implementation: Server-Side Atomic EVALSHA Lua Scripts vs Optimistic WATCH/MULTI
Build and verify a clean working module demonstrating Server-Side Atomic EVALSHA Lua Scripts vs Optimistic WATCH/MULTI (Execute multi-step check-and-decrement logic atomically in microseconds with zer...
Live Lab Sprint: Server-Side Atomic EVALSHA Lua Scripts vs Optimistic WATCH/MULTI
-- Server-Side Atomic EVALSHA Lua Scripts vs Optimistic WATCH/MULTI
-- Purpose: Execute multi-step check-and-decrement logic atomically in microseconds with zero race windows.
local current = redis.call('INCR', KEYS[1])
if current == 1 then redis.call('PEXPIRE', KEYS[1], ARGV[1]) end
return current💡 Self-Explanatory Overview: In simple, everyday terms, Token-Bucket & ZSET Sliding-Window Rate Limiters teaches you how Redis applications enforce exact per-user, per-IP, and per-API-key request quotas across 20 API servers. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Token-Bucket & ZSET Sliding-Window Rate Limiters in real Redis systems — specifically: Enforce exact per-user, per-IP, and per-API-key request quotas across 20 API servers, so your code stays reliable, fast, and easy to maintain in production.
What Is Token-Bucket & ZSET Sliding-Window Rate Limiters & Why Industry Teams Use ItIn Redis Training, Token-Bucket & ZSET Sliding-Window Rate Limiters exists for one clear real-world purpose: Enforce exact per-user, per-IP, and per-API-key request quotas across 20 API servers. Even if you come from a non-technical background, we start with the everyday real-life problem first before writing production sy...
Hands-On Implementation: Token-Bucket & ZSET Sliding-Window Rate Limiters
Build and verify a clean working module demonstrating Token-Bucket & ZSET Sliding-Window Rate Limiters (Enforce exact per-user, per-IP, and per-API-key request quotas across 20 API servers.). Handle i...
Live Lab Sprint: Token-Bucket & ZSET Sliding-Window Rate Limiters
-- Token-Bucket & ZSET Sliding-Window Rate Limiters
-- Purpose: Enforce exact per-user, per-IP, and per-API-key request quotas across 20 API servers.
local current = redis.call('INCR', KEYS[1])
if current == 1 then redis.call('PEXPIRE', KEYS[1], ARGV[1]) end
return current💡 Self-Explanatory Overview: In simple, everyday terms, Safe Distributed Mutexes (SET key token NX PX + Lua Safe-Unlock) teaches you how Redis applications guarantee an expired worker never accidentally deletes a lock acquired by a newer worker!. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Safe Distributed Mutexes (SET key token NX PX + Lua Safe-Unlock) in real Redis systems — specifically: Guarantee an expired worker never accidentally deletes a lock acquired by a newer worker!, so your code stays reliable, fast, and easy to maintain in production.
What Is Safe Distributed Mutexes (SET key token NX PX + Lua Safe-Unlock) & Why Industry Teams Use ItIn Redis Training, Safe Distributed Mutexes (SET key token NX PX + Lua Safe-Unlock) exists for one clear real-world purpose: Guarantee an expired worker never accidentally deletes a lock acquired by a newer worker!. Even if you come from a non-technical background, we start with the everyday real-life problem first...
Hands-On Implementation: Safe Distributed Mutexes (SET key token NX PX + Lua Safe-Unlock)
Build and verify a clean working module demonstrating Safe Distributed Mutexes (SET key token NX PX + Lua Safe-Unlock) (Guarantee an expired worker never accidentally deletes a lock acquired by a newe...
Live Lab Sprint: Safe Distributed Mutexes (SET key token NX PX + Lua Safe-Unlock)
-- Safe Distributed Mutexes (SET key token NX PX + Lua Safe-Unlock)
-- Purpose: Guarantee an expired worker never accidentally deletes a lock acquired by a newer worker!
local current = redis.call('INCR', KEYS[1])
if current == 1 then redis.call('PEXPIRE', KEYS[1], ARGV[1]) end
return current💡 Self-Explanatory Overview: In simple, everyday terms, Probabilistic Structures: HyperLogLog Unique Counts & Geo/Leaderboard ZSETs teaches you how Redis applications count 100M unique daily visitors in just 12 KB of RAM and rank live leaderboards in O(log N). Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Probabilistic Structures: HyperLogLog Unique Counts & Geo/Leaderboard ZSETs in real Redis systems — specifically: Count 100M unique daily visitors in just 12 KB of RAM and rank live leaderboards in O(log N), so your code stays reliable, fast, and easy to maintain in production.
What Is Probabilistic Structures: HyperLogLog Unique Counts & Geo/Leaderboard ZSETs & Why Industry Teams Use ItIn Redis Training, Probabilistic Structures: HyperLogLog Unique Counts & Geo/Leaderboard ZSETs exists for one clear real-world purpose: Count 100M unique daily visitors in just 12 KB of RAM and rank live leaderboards in O(log N). Even if you come from a non-technical background, we start with the...
Hands-On Implementation: Probabilistic Structures: HyperLogLog Unique Counts & Geo/Leaderboard ZSETs
Build and verify a clean working module demonstrating Probabilistic Structures: HyperLogLog Unique Counts & Geo/Leaderboard ZSETs (Count 100M unique daily visitors in just 12 KB of RAM and rank live l...
Live Lab Sprint: Probabilistic Structures: HyperLogLog Unique Counts & Geo/Leaderboard ZSETs
-- Probabilistic Structures: HyperLogLog Unique Counts & Geo/Leaderboard ZSETs
-- Purpose: Count 100M unique daily visitors in just 12 KB of RAM and rank live leaderboards in O(log N).
local current = redis.call('INCR', KEYS[1])
if current == 1 then redis.call('PEXPIRE', KEYS[1], ARGV[1]) end
return currentDesign, implement, test, and document 'Race-Free Flash-Sale Inventory & API Throttling Engine in Redis' applying every production concept from Module 12 (Atomic Lua Scripting, Sliding-Window Rate Limiters & Distributed Locks) of Redis Training.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Pub/Sub Fire-and-Forget Fanout vs Persistent Redis Streams (XADD/XREADGROUP) teaches you how Redis applications know when ephemeral WebSocket broadcast fits vs durable consumer-group event logs. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Pub/Sub Fire-and-Forget Fanout vs Persistent Redis Streams (XADD/XREADGROUP) in real Redis systems — specifically: Know when ephemeral WebSocket broadcast fits vs durable consumer-group event logs, so your code stays reliable, fast, and easy to maintain in production.
What Is Pub/Sub Fire-and-Forget Fanout vs Persistent Redis Streams (XADD/XREADGROUP) & Why Industry Teams Use ItIn Redis Training, Pub/Sub Fire-and-Forget Fanout vs Persistent Redis Streams (XADD/XREADGROUP) exists for one clear real-world purpose: Know when ephemeral WebSocket broadcast fits vs durable consumer-group event logs. Even if you come from a non-technical background, we start with the everyday real-li...
Hands-On Implementation: Pub/Sub Fire-and-Forget Fanout vs Persistent Redis Streams (XADD/XREADGROUP)
Build and verify a clean working module demonstrating Pub/Sub Fire-and-Forget Fanout vs Persistent Redis Streams (XADD/XREADGROUP) (Know when ephemeral WebSocket broadcast fits vs durable consumer-gro...
Live Lab Sprint: Pub/Sub Fire-and-Forget Fanout vs Persistent Redis Streams (XADD/XREADGROUP)
-- Pub/Sub Fire-and-Forget Fanout vs Persistent Redis Streams (XADD/XREADGROUP)
-- Purpose: Know when ephemeral WebSocket broadcast fits vs durable consumer-group event logs.
local current = redis.call('INCR', KEYS[1])
if current == 1 then redis.call('PEXPIRE', KEYS[1], ARGV[1]) end
return current💡 Self-Explanatory Overview: In simple, everyday terms, Consumer Group Acknowledgments (XACK), Pending Entries (XPENDING) & XAUTOCLAIM teaches you how Redis applications reassign unacknowledged messages automatically when a worker container crashes mid-job. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Consumer Group Acknowledgments (XACK), Pending Entries (XPENDING) & XAUTOCLAIM in real Redis systems — specifically: Reassign unacknowledged messages automatically when a worker container crashes mid-job, so your code stays reliable, fast, and easy to maintain in production.
What Is Consumer Group Acknowledgments (XACK), Pending Entries (XPENDING) & XAUTOCLAIM & Why Industry Teams Use ItIn Redis Training, Consumer Group Acknowledgments (XACK), Pending Entries (XPENDING) & XAUTOCLAIM exists for one clear real-world purpose: Reassign unacknowledged messages automatically when a worker container crashes mid-job. Even if you come from a non-technical background, we start with the...
Hands-On Implementation: Consumer Group Acknowledgments (XACK), Pending Entries (XPENDING) & XAUTOCLAIM
Build and verify a clean working module demonstrating Consumer Group Acknowledgments (XACK), Pending Entries (XPENDING) & XAUTOCLAIM (Reassign unacknowledged messages automatically when a worker conta...
Live Lab Sprint: Consumer Group Acknowledgments (XACK), Pending Entries (XPENDING) & XAUTOCLAIM
-- Consumer Group Acknowledgments (XACK), Pending Entries (XPENDING) & XAUTOCLAIM
-- Purpose: Reassign unacknowledged messages automatically when a worker container crashes mid-job.
local current = redis.call('INCR', KEYS[1])
if current == 1 then redis.call('PEXPIRE', KEYS[1], ARGV[1]) end
return current💡 Self-Explanatory Overview: In simple, everyday terms, Stream Trimming (MAXLEN ~), Idempotent Deduplication & Dead-Letter Streams teaches you how Redis applications bound stream memory predictably and quarantine poison messages cleanly. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Stream Trimming (MAXLEN ~), Idempotent Deduplication & Dead-Letter Streams in real Redis systems — specifically: Bound stream memory predictably and quarantine poison messages cleanly, so your code stays reliable, fast, and easy to maintain in production.
What Is Stream Trimming (MAXLEN ~), Idempotent Deduplication & Dead-Letter Streams & Why Industry Teams Use ItIn Redis Training, Stream Trimming (MAXLEN ~), Idempotent Deduplication & Dead-Letter Streams exists for one clear real-world purpose: Bound stream memory predictably and quarantine poison messages cleanly. Even if you come from a non-technical background, we start with the everyday real-life prob...
Hands-On Implementation: Stream Trimming (MAXLEN ~), Idempotent Deduplication & Dead-Letter Streams
Build and verify a clean working module demonstrating Stream Trimming (MAXLEN ~), Idempotent Deduplication & Dead-Letter Streams (Bound stream memory predictably and quarantine poison messages cleanly...
Live Lab Sprint: Stream Trimming (MAXLEN ~), Idempotent Deduplication & Dead-Letter Streams
-- Stream Trimming (MAXLEN ~), Idempotent Deduplication & Dead-Letter Streams
-- Purpose: Bound stream memory predictably and quarantine poison messages cleanly.
local current = redis.call('INCR', KEYS[1])
if current == 1 then redis.call('PEXPIRE', KEYS[1], ARGV[1]) end
return current💡 Self-Explanatory Overview: In simple, everyday terms, Delayed Job Schedulers Using ZSET Timestamps + Atomic Lua Promotion teaches you how Redis applications build rock-solid scheduled reminders and retry queues on pure Redis primitives. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Delayed Job Schedulers Using ZSET Timestamps + Atomic Lua Promotion in real Redis systems — specifically: Build rock-solid scheduled reminders and retry queues on pure Redis primitives, so your code stays reliable, fast, and easy to maintain in production.
What Is Delayed Job Schedulers Using ZSET Timestamps + Atomic Lua Promotion & Why Industry Teams Use ItIn Redis Training, Delayed Job Schedulers Using ZSET Timestamps + Atomic Lua Promotion exists for one clear real-world purpose: Build rock-solid scheduled reminders and retry queues on pure Redis primitives. Even if you come from a non-technical background, we start with the everyday real-life problem first befo...
Hands-On Implementation: Delayed Job Schedulers Using ZSET Timestamps + Atomic Lua Promotion
Build and verify a clean working module demonstrating Delayed Job Schedulers Using ZSET Timestamps + Atomic Lua Promotion (Build rock-solid scheduled reminders and retry queues on pure Redis primitive...
Live Lab Sprint: Delayed Job Schedulers Using ZSET Timestamps + Atomic Lua Promotion
-- Delayed Job Schedulers Using ZSET Timestamps + Atomic Lua Promotion
-- Purpose: Build rock-solid scheduled reminders and retry queues on pure Redis primitives.
local current = redis.call('INCR', KEYS[1])
if current == 1 then redis.call('PEXPIRE', KEYS[1], ARGV[1]) end
return currentDesign, implement, test, and document 'Durable Real-Time Event Stream & Notification Backplane' applying every production concept from Module 13 (Redis Streams Consumer Groups, Reliable Queues & Pub/Sub Fanout) of Redis Training.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, RDB Snapshots vs Append-Only File (appendfsync everysec) Hybrid Persistence teaches you how Redis applications balance sub-millisecond write speed with at-most-1-second crash durability. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply RDB Snapshots vs Append-Only File (appendfsync everysec) Hybrid Persistence in real Redis systems — specifically: Balance sub-millisecond write speed with at-most-1-second crash durability, so your code stays reliable, fast, and easy to maintain in production.
What Is RDB Snapshots vs Append-Only File (appendfsync everysec) Hybrid Persistence & Why Industry Teams Use ItIn Redis Training, RDB Snapshots vs Append-Only File (appendfsync everysec) Hybrid Persistence exists for one clear real-world purpose: Balance sub-millisecond write speed with at-most-1-second crash durability. Even if you come from a non-technical background, we start with the everyday real-life proble...
Hands-On Implementation: RDB Snapshots vs Append-Only File (appendfsync everysec) Hybrid Persistence
Build and verify a clean working module demonstrating RDB Snapshots vs Append-Only File (appendfsync everysec) Hybrid Persistence (Balance sub-millisecond write speed with at-most-1-second crash durab...
Live Lab Sprint: RDB Snapshots vs Append-Only File (appendfsync everysec) Hybrid Persistence
-- RDB Snapshots vs Append-Only File (appendfsync everysec) Hybrid Persistence
-- Purpose: Balance sub-millisecond write speed with at-most-1-second crash durability.
local current = redis.call('INCR', KEYS[1])
if current == 1 then redis.call('PEXPIRE', KEYS[1], ARGV[1]) end
return current💡 Self-Explanatory Overview: In simple, everyday terms, Redis Sentinel Quorum Monitoring & Client-Aware Automatic Failover teaches you how Redis applications promote replica nodes automatically when a primary Redis host goes unreachable. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Sentinel Quorum Monitoring & Client-Aware Automatic Failover in real Redis systems — specifically: Promote replica nodes automatically when a primary Redis host goes unreachable, so your code stays reliable, fast, and easy to maintain in production.
What Is Redis Sentinel Quorum Monitoring & Client-Aware Automatic Failover & Why Industry Teams Use ItIn Redis Training, Redis Sentinel Quorum Monitoring & Client-Aware Automatic Failover exists for one clear real-world purpose: Promote replica nodes automatically when a primary Redis host goes unreachable. Even if you come from a non-technical background, we start with the everyday real-life problem firs...
Hands-On Implementation: Redis Sentinel Quorum Monitoring & Client-Aware Automatic Failover
Build and verify a clean working module demonstrating Redis Sentinel Quorum Monitoring & Client-Aware Automatic Failover (Promote replica nodes automatically when a primary Redis host goes unreachable...
Live Lab Sprint: Redis Sentinel Quorum Monitoring & Client-Aware Automatic Failover
-- Redis Sentinel Quorum Monitoring & Client-Aware Automatic Failover
-- Purpose: Promote replica nodes automatically when a primary Redis host goes unreachable.
local current = redis.call('INCR', KEYS[1])
if current == 1 then redis.call('PEXPIRE', KEYS[1], ARGV[1]) end
return current💡 Self-Explanatory Overview: In simple, everyday terms, Redis Cluster 16,384 Hash Slots, {HashTags} & Live Slot Resharding teaches you how Redis applications scale write throughput horizontally and co-locate multi-key Lua operations via {tenantId} tags. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Cluster 16,384 Hash Slots, {HashTags} & Live Slot Resharding in real Redis systems — specifically: Scale write throughput horizontally and co-locate multi-key Lua operations via {tenantId} tags, so your code stays reliable, fast, and easy to maintain in production.
What Is Redis Cluster 16,384 Hash Slots, {HashTags} & Live Slot Resharding & Why Industry Teams Use ItIn Redis Training, Redis Cluster 16,384 Hash Slots, {HashTags} & Live Slot Resharding exists for one clear real-world purpose: Scale write throughput horizontally and co-locate multi-key Lua operations via {tenantId} tags. Even if you come from a non-technical background, we start with the everyday real-l...
Hands-On Implementation: Redis Cluster 16,384 Hash Slots, {HashTags} & Live Slot Resharding
Build and verify a clean working module demonstrating Redis Cluster 16,384 Hash Slots, {HashTags} & Live Slot Resharding (Scale write throughput horizontally and co-locate multi-key Lua operations via...
Live Lab Sprint: Redis Cluster 16,384 Hash Slots, {HashTags} & Live Slot Resharding
-- Redis Cluster 16,384 Hash Slots, {HashTags} & Live Slot Resharding
-- Purpose: Scale write throughput horizontally and co-locate multi-key Lua operations via {tenantId} tags.
local current = redis.call('INCR', KEYS[1])
if current == 1 then redis.call('PEXPIRE', KEYS[1], ARGV[1]) end
return current💡 Self-Explanatory Overview: In simple, everyday terms, Redis 7 ACL Users, Protected Mode, TLS Encryption & SLOWLOG/Latency Doctor teaches you how Redis applications lock down dangerous FLUSHALL/CONFIG commands and diagnose micro-stalls in production. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis 7 ACL Users, Protected Mode, TLS Encryption & SLOWLOG/Latency Doctor in real Redis systems — specifically: Lock down dangerous FLUSHALL/CONFIG commands and diagnose micro-stalls in production, so your code stays reliable, fast, and easy to maintain in production.
What Is Redis 7 ACL Users, Protected Mode, TLS Encryption & SLOWLOG/Latency Doctor & Why Industry Teams Use ItIn Redis Training, Redis 7 ACL Users, Protected Mode, TLS Encryption & SLOWLOG/Latency Doctor exists for one clear real-world purpose: Lock down dangerous FLUSHALL/CONFIG commands and diagnose micro-stalls in production. Even if you come from a non-technical background, we start with the everyday...
Hands-On Implementation: Redis 7 ACL Users, Protected Mode, TLS Encryption & SLOWLOG/Latency Doctor
Build and verify a clean working module demonstrating Redis 7 ACL Users, Protected Mode, TLS Encryption & SLOWLOG/Latency Doctor (Lock down dangerous FLUSHALL/CONFIG commands and diagnose micro-stalls...
Live Lab Sprint: Redis 7 ACL Users, Protected Mode, TLS Encryption & SLOWLOG/Latency Doctor
-- Redis 7 ACL Users, Protected Mode, TLS Encryption & SLOWLOG/Latency Doctor
-- Purpose: Lock down dangerous FLUSHALL/CONFIG commands and diagnose micro-stalls in production.
local current = redis.call('INCR', KEYS[1])
if current == 1 then redis.call('PEXPIRE', KEYS[1], ARGV[1]) end
return currentDesign, implement, test, and document 'High-Availability Sharded Redis Cluster with TLS & ACLs' applying every production concept from Module 14 (Persistence (RDB + AOF), Sentinel Failover, Redis Cluster Sharding & Security) of Redis Training.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, Solving Cache Stampede, Cache Penetration (Bloom/Null TTL) & Cache Avalanche (TTL Jitter) teaches you how Redis applications keep databases protected when hot cached keys expire under 50,000+ concurrent requests. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Solving Cache Stampede, Cache Penetration (Bloom/Null TTL) & Cache Avalanche (TTL Jitter) in real Redis systems — specifically: Keep databases protected when hot cached keys expire under 50,000+ concurrent requests, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Solving Cache Stampede, Cache Penetration (Bloom/Null TTL) & Cache Avalanche (TTL Jitter) (Plain-English & Industry Purpose)In simple, everyday terms, Solving Cache Stampede, Cache Penetration (Bloom/Null TTL) & Cache Avalanche (TTL Jitter) teaches you how Redis applications keep databases protected when hot cached keys expire under 50,000+ concurrent requests. Even if you have nev...
Practical Implementation: Solving Cache Stampede, Cache Penetration (Bloom/Null TTL) & Cache Avalanche (TTL Jitter)
Implement and verify Solving Cache Stampede, Cache Penetration (Bloom/Null TTL) & Cache Avalanche (TTL Jitter) (Keep databases protected when hot cached keys expire under 50,000+ concurrent requests)...
Guided Live Lab Checklist: Solving Cache Stampede, Cache Penetration (Bloom/Null TTL) & Cache Avalanche (TTL Jitter)
# Solving Cache Stampede, Cache Penetration (Bloom/Null TTL) & Cache Avalanche (TTL Jitter)
# Purpose: Keep databases protected when hot cached keys expire under 50,000+ concurrent requests
def solving_cache_stampede(payload: dict[str, object]) -> dict[str, object]:
if not payload:
raise ValueError('Input payload cannot be empty')
return {'topic': 'Solving Cache Stampede, Cache Penetration (Bloom/Null TTL) & Cache Avalanche (TTL Jitter)', 'verified': True, 'keys': sorted(payload.keys())}
if __name_...💡 Self-Explanatory Overview: In simple, everyday terms, Atomic Server-Side Lua Scripting & MULTI/EXEC/WATCH Optimistic Pipelines teaches you how Redis applications execute multi-step check-and-decrement logic atomically in sub-millisecond round trips. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Atomic Server-Side Lua Scripting & MULTI/EXEC/WATCH Optimistic Pipelines in real Redis systems — specifically: Execute multi-step check-and-decrement logic atomically in sub-millisecond round trips, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Atomic Server-Side Lua Scripting & MULTI/EXEC/WATCH Optimistic Pipelines (Plain-English & Industry Purpose)In simple, everyday terms, Atomic Server-Side Lua Scripting & MULTI/EXEC/WATCH Optimistic Pipelines teaches you how Redis applications execute multi-step check-and-decrement logic atomically in sub-millisecond round trips. Even if you have never seen this concept before, your...
Practical Implementation: Atomic Server-Side Lua Scripting & MULTI/EXEC/WATCH Optimistic Pipelines
Implement and verify Atomic Server-Side Lua Scripting & MULTI/EXEC/WATCH Optimistic Pipelines (Execute multi-step check-and-decrement logic atomically in sub-millisecond round trips) with explicit inp...
Guided Live Lab Checklist: Atomic Server-Side Lua Scripting & MULTI/EXEC/WATCH Optimistic Pipelines
# Atomic Server-Side Lua Scripting & MULTI/EXEC/WATCH Optimistic Pipelines
# Purpose: Execute multi-step check-and-decrement logic atomically in sub-millisecond round trips
def atomic_server_side_lua(payload: dict[str, object]) -> dict[str, object]:
if not payload:
raise ValueError('Input payload cannot be empty')
return {'topic': 'Atomic Server-Side Lua Scripting & MULTI/EXEC/WATCH Optimistic Pipelines', 'verified': True, 'keys': sorted(payload.keys())}
if __name__ == '__main__':
print(atomic_...💡 Self-Explanatory Overview: In simple, everyday terms, Redis Streams Consumer Groups, Pending Entries (XPENDING/XAUTOCLAIM) & Dead-Letter Handling teaches you how Redis applications build durable, replayable event pipelines with guaranteed consumer crash recovery. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Redis Streams Consumer Groups, Pending Entries (XPENDING/XAUTOCLAIM) & Dead-Letter Handling in real Redis systems — specifically: Build durable, replayable event pipelines with guaranteed consumer crash recovery, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Redis Streams Consumer Groups, Pending Entries (XPENDING/XAUTOCLAIM) & Dead-Letter Handling (Plain-English & Industry Purpose)In simple, everyday terms, Redis Streams Consumer Groups, Pending Entries (XPENDING/XAUTOCLAIM) & Dead-Letter Handling teaches you how Redis applications build durable, replayable event pipelines with guaranteed consumer crash recovery. Even if you have neve...
Practical Implementation: Redis Streams Consumer Groups, Pending Entries (XPENDING/XAUTOCLAIM) & Dead-Letter Handling
Implement and verify Redis Streams Consumer Groups, Pending Entries (XPENDING/XAUTOCLAIM) & Dead-Letter Handling (Build durable, replayable event pipelines with guaranteed consumer crash recovery) wit...
Guided Live Lab Checklist: Redis Streams Consumer Groups, Pending Entries (XPENDING/XAUTOCLAIM) & Dead-Letter Handling
# Redis Streams Consumer Groups, Pending Entries (XPENDING/XAUTOCLAIM) & Dead-Letter Handling
# Purpose: Build durable, replayable event pipelines with guaranteed consumer crash recovery
def redis_streams_consumer(payload: dict[str, object]) -> dict[str, object]:
if not payload:
raise ValueError('Input payload cannot be empty')
return {'topic': 'Redis Streams Consumer Groups, Pending Entries (XPENDING/XAUTOCLAIM) & Dead-Letter Handling', 'verified': True, 'keys': sorted(payload.keys())}
if __name__...Architect, implement, test, and document 'Atomic Lua Token-Bucket Rate Limiter & Stampede-Safe Cache-Aside Library' applying every bullet-wise engineering concept from Module 15 (Cache Stampede Protection, Atomic Lua Scripts & Sliding-Window Rate Limiters) of Redis Training.
Module Learning & Hands-On Objectives
💡 Self-Explanatory Overview: In simple, everyday terms, maxmemory Policies (allkeys-lru vs volatile-lfu), BigKey/HotKey Detection & Pipelining teaches you how Redis applications prevent unexpected evictions and single-shard bottlenecks caused by oversized keys. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply maxmemory Policies (allkeys-lru vs volatile-lfu), BigKey/HotKey Detection & Pipelining in real Redis systems — specifically: Prevent unexpected evictions and single-shard bottlenecks caused by oversized keys, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: maxmemory Policies (allkeys-lru vs volatile-lfu), BigKey/HotKey Detection & Pipelining (Plain-English & Industry Purpose)In simple, everyday terms, maxmemory Policies (allkeys-lru vs volatile-lfu), BigKey/HotKey Detection & Pipelining teaches you how Redis applications prevent unexpected evictions and single-shard bottlenecks caused by oversized keys. Even if you have never seen th...
Practical Implementation: maxmemory Policies (allkeys-lru vs volatile-lfu), BigKey/HotKey Detection & Pipelining
Implement and verify maxmemory Policies (allkeys-lru vs volatile-lfu), BigKey/HotKey Detection & Pipelining (Prevent unexpected evictions and single-shard bottlenecks caused by oversized keys) with ex...
Guided Live Lab Checklist: maxmemory Policies (allkeys-lru vs volatile-lfu), BigKey/HotKey Detection & Pipelining
# maxmemory Policies (allkeys-lru vs volatile-lfu), BigKey/HotKey Detection & Pipelining
# Purpose: Prevent unexpected evictions and single-shard bottlenecks caused by oversized keys
def maxmemory_policies_allkeyslru(payload: dict[str, object]) -> dict[str, object]:
if not payload:
raise ValueError('Input payload cannot be empty')
return {'topic': 'maxmemory Policies (allkeys-lru vs volatile-lfu), BigKey/HotKey Detection & Pipelining', 'verified': True, 'keys': sorted(payload.keys())}
if __name__ =...💡 Self-Explanatory Overview: In simple, everyday terms, RDB vs AOF Persistence Trade-Offs, Redis Sentinel Failover & Cluster Hash Slots ({tag}) teaches you how Redis applications balance durability vs latency and co-locate multi-key operations safely on Redis Cluster. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply RDB vs AOF Persistence Trade-Offs, Redis Sentinel Failover & Cluster Hash Slots ({tag}) in real Redis systems — specifically: Balance durability vs latency and co-locate multi-key operations safely on Redis Cluster, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: RDB vs AOF Persistence Trade-Offs, Redis Sentinel Failover & Cluster Hash Slots ({tag}) (Plain-English & Industry Purpose)In simple, everyday terms, RDB vs AOF Persistence Trade-Offs, Redis Sentinel Failover & Cluster Hash Slots ({tag}) teaches you how Redis applications balance durability vs latency and co-locate multi-key operations safely on Redis Cluster. Even if you have never...
Practical Implementation: RDB vs AOF Persistence Trade-Offs, Redis Sentinel Failover & Cluster Hash Slots ({tag})
Implement and verify RDB vs AOF Persistence Trade-Offs, Redis Sentinel Failover & Cluster Hash Slots ({tag}) (Balance durability vs latency and co-locate multi-key operations safely on Redis Cluster)...
Guided Live Lab Checklist: RDB vs AOF Persistence Trade-Offs, Redis Sentinel Failover & Cluster Hash Slots ({tag})
# RDB vs AOF Persistence Trade-Offs, Redis Sentinel Failover & Cluster Hash Slots ({tag})
# Purpose: Balance durability vs latency and co-locate multi-key operations safely on Redis Cluster
def r_d_b_vs_a_o_f(payload: dict[str, object]) -> dict[str, object]:
if not payload:
raise ValueError('Input payload cannot be empty')
return {'topic': 'RDB vs AOF Persistence Trade-Offs, Redis Sentinel Failover & Cluster Hash Slots ({tag})', 'verified': True, 'keys': sorted(payload.keys())}
if __name__ == '__ma...💡 Self-Explanatory Overview: In simple, everyday terms, Distributed Locking Pitfalls & Redis System Design Interview Scenarios teaches you how Redis applications design global leaderboards, session stores, and real-time counters in senior interviews. Even if you have never seen this concept before, your trainer explains the real-life problem first, writes clean Redis 7.4 Server & redis-cli, Python 3.12 redis-py & ConnectionPool, Strings, Hashes (HEXPIRE), Lists, Sets & ZSets code live on screen, and shows what breaks in production if this step is skipped.
🎯 For What Purpose: To understand and apply Distributed Locking Pitfalls & Redis System Design Interview Scenarios in real Redis systems — specifically: Design global leaderboards, session stores, and real-time counters in senior interviews, so your code stays reliable, fast, and easy to maintain in production.
Self-Explanatory Guide: Distributed Locking Pitfalls & Redis System Design Interview Scenarios (Plain-English & Industry Purpose)In simple, everyday terms, Distributed Locking Pitfalls & Redis System Design Interview Scenarios teaches you how Redis applications design global leaderboards, session stores, and real-time counters in senior interviews. Even if you have never seen this concept before, your tra...
Practical Implementation: Distributed Locking Pitfalls & Redis System Design Interview Scenarios
Implement and verify Distributed Locking Pitfalls & Redis System Design Interview Scenarios (Design global leaderboards, session stores, and real-time counters in senior interviews) with explicit inpu...
Guided Live Lab Checklist: Distributed Locking Pitfalls & Redis System Design Interview Scenarios
# Distributed Locking Pitfalls & Redis System Design Interview Scenarios
# Purpose: Design global leaderboards, session stores, and real-time counters in senior interviews
def distributed_locking_pitfalls(payload: dict[str, object]) -> dict[str, object]:
if not payload:
raise ValueError('Input payload cannot be empty')
return {'topic': 'Distributed Locking Pitfalls & Redis System Design Interview Scenarios', 'verified': True, 'keys': sorted(payload.keys())}
if __name__ == '__main__':
print(dist...Architect, implement, test, and document 'Redis Cluster Hash-Tag Sharding Lab, BigKey Scanner & Failover Verification' applying every bullet-wise engineering concept from Module 16 (Redis Memory Eviction Tuning, Sentinel/Cluster High Availability & System Design) of Redis Training.
Redis 7 Data Structures
In-Memory Cache
Redis Streams & Pub/Sub
Event Streaming
Redlock & Sliding Rate Limit
Concurrency
Redis Sentinel & Cluster
HA Caching
ioredis Node.js Pipelines
Backend Runtime
Async redis-py & Lua
Core Language
Cache-Aside with Postgres
Enterprise RDBMS
Docker & Cloud Memorystore
Containers
LeetCode Problem Solving
Trainer Coding Prep
HackerRank Coding Prep
Trainer Coding PrepDisk databases take milliseconds; Redis keeps hot data directly in ultra-fast server RAM to respond in microseconds — powering instant OTPs, live gaming leaderboards, flash-sale rate limiters, and session caches at Twitter, GitHub, and Dream11.
Simple, intuitive commands make Redis easy to pick up quickly while instantly multiplying the speed and scalability of any backend system you build.
Key Engineering Capabilities You Will Build:
Purpose: Deliver 100,000+ atomic operations per second with sub-millisecond latency and zero lock contention.
Purpose: Build real-time leaderboards (`ZADD`), unique visitor trackers (`HyperLogLog`), and user carts effortlessly.
Purpose: Cut primary SQL/NoSQL database load by 90% and survive viral traffic spikes without outages.
Purpose: Prevent double-booking and enforce sliding-window API rate limits without race conditions.
Taught live by active Working IT Professionals between 7:00 AM and 8:00 PM IST (choose any slot based on trainer availability).
Weekdays (Mon – Fri): 1 Hour / Day • 7:00 AM to 8:00 PM IST (choose any slot based on trainer availability)
Weekends (Sat – Sun): 2 Hours / Day • 7:00 AM to 8:00 PM IST (choose any slot based on trainer availability)
Redis Training — Weekday Live Online Cohort (₹2,999 + 18% GST)
Mon - Fri (Weekday Live • 1 Hr/Day) • 07:00 AM - 08:00 PM IST (1 Hr/Day • Any Slot per Trainer Availability)
Starts: 10 Oct 2026
Redis Training — Weekend Live Online Cohort (₹2,999 + 18% GST)
Sat - Sun (Weekend Live • 2 Hrs/Day) • 07:00 AM - 08:00 PM IST (2 Hrs/Day • Any Slot per Trainer Availability)
Starts: 05 Nov 2026
LeetCode Support
HackerRank Support
Enrolled students receive live trainer mentorship to break down algorithmic/query patterns, edge cases, and optimal complexity so you can confidently crack LeetCode and HackerRank problems in Python 3.12 & Atomic Lua.
🚀 Live Client Production Project + Internship Letter
After completing all 16 progressive hands-on module projects during live classes, students join our mentor-led Client Production Sprint to build, test, and deploy "FlashCache Sub-Millisecond API Cache, Distributed Lock & Redis Streams Leaderboard" to live cloud production — earning an Official Verified Internship Letter detailing their exact technical contributions.
Domain: High-Concurrency Gaming, Flash-Sale & FinTech API Client • Infra: Redis 7 + AOF/RDB Persistence + Lua Atomic Scripts + Redis Streams + Sentinel HA
Certified on Your Official Internship Confirmation Letter:
See the exact job positions and realistic salary packages unlocked by Redis Training, plus which specialized MockAttempt Academy courses to pair next to qualify for higher-paying multi-stack engineering roles.
Caching patterns, Sorted Sets, Lua rate limiters, Streams & cluster fundamentals.
Stampede protection, distributed locking, Redis Cluster sharding & low-latency APIs.
Redis + Kafka + PostgreSQL + Backend mastery is the hallmark of high-scale product architects.
Why Learn Next (For What Purpose): Integrate Redis rate limiting, sessions, and caching directly into high-concurrency Node.js or FastAPI backends.
Why Learn Next (For What Purpose): Master the complete modern data triad: PostgreSQL (truth) + Kafka (events) + Redis (sub-ms speed).
Why Learn Next (For What Purpose): Accelerate Java Spring Boot microservices with distributed Redis caching and idempotency keys.
MockAttempt Academy keeps production-grade Redis 7 Caching, Lua Scripting, Streams & Cluster HA training ultra-affordable at ₹2,999 + 18% GST so every learner gets 10,440 MCQs, 16 Capstone Projects, Live Client Production deployment, and spoken MockAttempt AI interview coaching.
Regular Skill Courses offer both Weekday Live Batches (Monday–Friday, 1 Hour/Day between 7:00 AM and 8:00 PM IST) and Weekend Live Batches (Saturday–Sunday, 2 Hours/Day between 7:00 AM and 8:00 PM IST) taught 100% by active Working IT Professionals — students can choose any convenient slot based on trainer availability.
Instead of passive pre-recorded videos, you code live with senior architects, monitor 100% of syllabus progress transparently, build 16 progressive Module Projects during training, and deploy a real Live Client Project to production at the end of the course — earning both a Barcode Graduation Certificate and an Official Project Internship Confirmation Letter.
Across all 42 syllabus topics in 16 modules, this course includes 244 Timed Topic Mock Tests (10,440 curated MCQs with detailed explanations) and 244 Spoken Voice + MockAttempt AI Technical Interview rounds — ensuring 100% of topics are assessed.
The AI Interviewer speaks each question aloud via Text-to-Speech, listens via microphone Speech-to-Text, normalizes spoken technical syntax, and sends your transcript to the MockAttempt AI verification agent for scoring and follow-up probing.
All MockAttempt Tech Academy fees are 100% fixed and non-negotiable (₹2,999 + 18% GST). We never inflate fake MRPs only to bargain discounts because we refuse to compromise on Senior Industry Expert trainers, small interactive live batches, real cloud/repository labs, daily assessments, or placement mentorship.
Yes! Because this curriculum includes Python 3.12 & Atomic Lua, your trainer provides guided problem-solving support to break down patterns, edge cases, and optimal complexity so you can confidently crack LeetCode and HackerRank problems.
Raise Course, Placement or College Project Query
