Why is Redis So Fast?
We all know it’s fast. But why and how? And why can’t the traditional SQL database use the same techniques to improve speed?
Hello subscribers!
Let’s talk about Redis. We all know it’s fast. But why and how? And why can’t the traditional SQL database use the same techniques to improve speed?
Whether you're prepping for a technical interview at a big tech company or optimizing your own systems, understanding Redis's high-performance architecture is crucial. Let’s explore why Redis is so powerful and how it achieves its incredible speed.
Use Cases
First off, Redis excels in use cases requiring high-speed data access, such as caching, real-time analytics, session management, and leaderboards. For these scenarios, Redis's data structures can perform faster than SQL databases.
SQL databases are better suited for complex querying, transactions, and maintaining relational integrity. While they may be slower in some scenarios, they provide powerful features for relational data management that Redis doesn’t provide.
With the use cases in mind, let’s explore why Redis is designed the way it is to better handle its intended use cases.
Single-Threaded Model and I/O Multiplexing
IO multiplexing is a model that allows a single thread to monitor multiple file descriptors—like network sockets—at once. This means Redis can handle multiple client connections simultaneously without creating a separate thread or process for each one, saving valuable resources. This is in contrast to SQL databases which typically are multi-threaded.
Redis operates using a single-threaded event loop (similar to Node.js or Haskell), which simplifies concurrency control and avoids the overhead of managing multiple threads. This design reduces context switching and locking, leading to faster operations. It can handle many connections efficiently because of its fast, non-blocking I/O.
Traditional SQL databases typically use context switching between threads involving saving and loading states, which can be computationally expensive. Additionally, threads need to acquire locks and manage shared data, which can introduce contention and reduce performance.
Behind the scene, Redis leverages the operating system's IO multiplexing mechanisms (like epoll on Linux) to ensure it can manage numerous client requests without blocking the entire service. This non-blocking, asynchronous mode is a key part of what makes Redis so fast.
In-Memory Storage
One of the most obvious reasons Redis is blazing fast is that it stores data in memory rather than on disk. The difference in read/write speeds between memory and disk is significant, with memory operating at a much higher bandwidth. This allows Redis to support a much higher level of concurrent operations.
But why is disk storage so much slower? The answer lies in the inherent design of disks. Traditional disks involve mechanical delays, such as seeking (moving the read/write head to the correct track) and rotational latency (waiting for the disk to rotate to the correct position). These steps take time—especially with mechanical hard drives—and can severely bottleneck performance.
In contrast, memory doesn't suffer from these mechanical delays. Access times are in the nanosecond range, compared to milliseconds for disk access. This is a core reason why Redis, which operates entirely in memory, is so much faster.
Simple and Efficient Data Structure
Redis offers specialized data structures (e.g., strings, lists, sets, sorted sets, hashes) that are optimized for in-memory storage. Operations on these data structures are highly efficient and often have time complexities like O(1) or O(log n). Here are some of the supported data structures and their time complexities.
1. Strings
Set a value (SET): O(1)
Get a value (GET): O(1)
Append to a string (APPEND): O(1)
Get the length of a string (STRLEN): O(1)
Increment/Decrement a value (INCR, DECR): O(1)
2. Lists
Push elements to the head (LPUSH): O(1)
Push elements to the tail (RPUSH): O(1)
Pop elements from the head (LPOP): O(1)
Pop elements from the tail (RPOP): O(1)
Get an element by index (LINDEX): O(n) (where n is the index)
Get a range of elements (LRANGE): O(S + N) (where S is the start index and N is the number of elements retrieved)
Remove elements by value (LREM): O(n)
Trim the list (LTRIM): O(n) (where n is the number of elements removed)
3. Sets
Add a member (SADD): O(1)
Remove a member (SREM): O(1)
Check if a member exists (SISMEMBER): O(1)
Get the number of members (SCARD): O(1)
Get all members (SMEMBERS): O(N) (where N is the number of elements in the set)
Set operations (union, intersection, difference: SUNION, SINTER, SDIFF): O(N) (where N is the total number of elements across the input sets)
4. Sorted Sets (Zsets)
Add a member with a score (ZADD): O(log N) (where N is the number of elements in the sorted set)
Remove a member (ZREM): O(log N)
Increment the score of a member (ZINCRBY): O(log N)
Get a range of members by rank (ZRANGE, ZREVRANGE): O(log N + M) (where M is the number of elements returned)
Get a range of members by score (ZRANGEBYSCORE, ZREVRANGEBYSCORE): O(log N + M)
Remove members by rank or score (ZREMRANGEBYRANK, ZREMRANGEBYSCORE): O(log N + M)
Get the rank of a member (ZRANK, ZREVRANK): O(log N)
Get the number of members in a range (ZCOUNT): O(log N)
5. Hashes
Set a field (HSET): O(1)
Get a field (HGET): O(1)
Delete a field (HDEL): O(1)
Get all fields and values (HGETALL): O(N) (where N is the number of fields)
Get all field names (HKEYS): O(N)
Get all values (HVALS): O(N)
Get the number of fields (HLEN): O(1)
Increment the value of a field (HINCRBY): O(1)
6. Bitmaps (treated as a binary string)
Set a bit (SETBIT): O(1)
Get a bit (GETBIT): O(1)
Bitwise operations (BITOP): O(N) (where N is the number of bits in the largest bitmap)
Count the number of set bits (BITCOUNT): O(N) (where N is the number of bits)
7. HyperLogLog
Add elements (PFADD): O(1)
Count the approximate cardinality (PFCOUNT): O(1)
Merge multiple HyperLogLogs (PFMERGE): O(1)
8. Geospatial Indexes (based on Sorted Sets)
Add a location (GEOADD): O(log N)
Get the distance between two members (GEODIST): O(log N)
Get members within a radius (GEORADIUS, GEORADIUSBYMEMBER): O(log N + M) (where M is the number of elements returned)
9. Streams
Append an entry (XADD): O(1)
Get entries by range (XRANGE, XREVRANGE): O(M) (where M is the number of elements returned)
Read entries with blocking (XREAD): O(M)
Get stream length (XLEN): O(1)
Get information about a stream (XINFO): O(1)
This is in contrast to traditional SQL databases where the fastest operation is typically a primary key look up which has a time complexity of O(log n). More complex queries may involve multiple disk reads/writes, especially for joins, aggregations are much slower. Again this is due to the nature of the intended use cases.
RESP: Redis’s Custom Network Protocol
To further enhance performance, Redis uses a custom network communication protocol called RESP (REdis Serialization Protocol). This protocol is simple yet highly efficient, reducing the overhead associated with network communication and improving data transfer speeds. Key features of the RESP protocol:
Simplicity:
RESP is very straightforward. The commands sent by the client and the responses from the server are easy to understand and process.
For example, a command like GET mykey is sent as a simple string, and the server responds with the value of mykey if it exists.
Minimal Overhead:
Because RESP is so simple, it doesn't take much time or computing power to parse and process commands and responses. This means Redis can handle more requests in less time.
Efficiency:
RESP uses basic data types like strings, integers, arrays, and errors. These are easy to serialize (convert into a format for transmission) and deserialize (convert back into usable data), which makes communication fast.
Comparison between RESP and traditional SQL protocols:
