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Redis is an open source, high-performance, advanced data structure store. It is a popular key-value store that supports a wide variety of data structures (including strings, hashes, lists, sets, and sorted sets). Redis is used in a variety of use cases including, but not limited to, caching, distributed data aggregation, and message queuing. As a result, its source code provides a great starting point for exploring how data is stored in a key-value store.
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Most of Redis’ source code is written in C, the lower-level language that offers a degree of control and efficiency not found in other languages. Redis mntns a simple and modular architecture that makes it strghtforward to navigate and debug. At the heart of this architecture is a dictionary-like data structure, referred to as “dict” in the source code, which provides an efficient mapping from keys to values.
The dict data structure is organized as an array of hash tables. When a user puts a key-value pr into the store, the key is hashed to obtn an array index. The corresponding hash table is then consulted to determine whether the key is already present. If the key is present, the value is updated. Otherwise, a new entry is created and added to the hash table.
This two-level structure offers great efficiency: lookups and updates are relatively fast as they are only O(1). However, it also means that as the size of the store increases, the hash tables need to be periodically resized. To avoid this, Redis mntns two separate “active” and “inactive” hash tables. When the number of elements in the active hash table reaches a certn threshold, the data is copied to an inactive hash table and then the inactive hash table becomes the active one. This process reduces the number of resizing operations and improves overall efficiency.
Redis also uses a variety of techniques to ensure that data remns consistent. For instance, it imposes strict write ordering so that any data written to disk comes after any data that has been written to memory. On top of this, Redis also uses a variety of techniques to ensure that data is correctly persisted between restarts.
Exploring Redis’ source code can give us insight into how a key-value store works at a low level. The dict data structure, the internal hash tables, and the mechanisms used to ensure data consistency are all key components of a reliable key-value store. By understanding each of these aspects, we can better answer the question, “What is the source of caching?”
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網站欄目：深入走進Redis源碼，探索緩存的本源（深挖redis源碼）
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