Debugging overloaded Redis servers
What is Redis?
From the docs, Redis (“REmote DIctionary Service”)
The open-source, in-memory data store used by millions of developers as a cache, vector database, document database, streaming engine, and message broker.
Debugging Redis Servers
You have 4 Redis servers that you’re using for caching user’s data, but some of the servers are more overloaded than the others. How would you debug that?
The following steps can be taken to debug the overloaded servers:
-
Analyzing Redis SlowLog: Enabling the slowlog on each server to capture information about slow-running commands and identify specific operations causing performance bottlenecks on overloaded servers. We can use
MONITORcommand inredis-clito get real-time insights into Redis commands being executed on each server. -
Monitoring Server Loads: We can configure monitoring tools with Redis server to monitor information such as network traffic, CPU and memory usage, and concurrent connections to each server. We can also get complete performance information of each server by running
INFOcommand directly inredis-cli -
Analyzing Key Distribution: We can look into key distribution strategy. If keys are not evenly distributed across servers, some might become overloaded while others remain idle. If we're using any hashing mechanism, we can analyze that as well as that could be one of the problems for uneven of the keys.
-
Looking into Client Applications: We can take a look into client application caching strategies. Imbalanced access patterns from clients can overload specific servers. To mitigate this, we can implement caching strategy to reduce load on overloaded Redis servers. We can also investigate into key access patterns, if the data is frequently access, we can shard data across multiple Redis servers.
-
Inspecting available resources: We can inspect if all the Redis servers have similar hardware specifications (CPU, mem etc). Insufficient resources on a particular server could lead to overload. We can also check background processes to make sure they're not consuming resources on overloaded servers.
There are other things can be considered to take look into such as data sharding mechanism, keys expiration and eviction policies to address issues associated with overloaded Redis servers.
Here's diagram depicting scenario with 4 Redis servers:
+--------------------+ +--------------------+ +--------------------+ +--------------------+
| Client Application | ---- | Redis Server 1 (A) | ---- | Redis Server 2 (B) | ---- | Redis Server 3 (C) | ---- | Redis Server 4 (D) |
+--------------------+ +--------------------+ +--------------------+ +--------------------+
| | | | |
| Writes/Reads User | | | | |
| Data | | | | |
| | | | | |
v v v v v
+--------------------+ +--------------------+ +--------------------+ +--------------------+
| Redis Cluster | | Cache | | Cache | | Cache | | Cache |
| (Optional) | | | | | | | | |
+--------------------+ +--------------------+ +--------------------+ +--------------------+
| | | | |
| Monitoring Tool | <---- Monitors Metrics | <---- Monitors Metrics | <---- Monitors Metrics | <---- Monitors Metrics |
| | | | |
v v v v v
+--------------------+ +--------------------+ +--------------------+ +--------------------+
| Monitoring Server | | | | | | | | |
+--------------------+ +--------------------+ +--------------------+ +--------------------+
Legend:
- Client Application: Sends data writes and reads to the Redis servers.
- Redis Server (A, B, C, D): Individual servers storing user data in the cache.
- Redis Cluster (Optional): A cluster configuration for horizontal scaling (not required for basic setup).
- Cache: Represents the in-memory data store on each Redis server.
- Monitoring Tool: Tracks server metrics like CPU, memory, and network usage.
- Monitoring Server: Centralized server to aggregate monitoring data (optional).