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Cassandra database storage structure _ cassandra database data write, read and delete
**Foreword**
This article delves into the data storage mechanisms in Cassandra, covering both in-memory and on-disk data structures. One of Cassandra’s standout features is its exceptional write performance. While this might seem surprising at first, it's not solely due to its data structure but rather a combination of its efficient writing mechanism. The article also explores the factors that influence read performance and how Cassandra has optimized for these challenges.
Cassandra's data storage is primarily organized into three key components:
1. **CommitLog**: This acts as a durable log that records all client-submitted data and operations. It ensures that data can be recovered if it hasn't been flushed to disk yet.
2. **Memtable**: A memory-resident structure where user data is temporarily stored before being written to disk. It uses a ConcurrentSkipListMap to manage keys and column families efficiently.
3. **SSTable (Sorted String Table)**: These are immutable files on disk that store the actual data, along with index and filter structures to optimize read operations.
**CommitLog Data Format**
The CommitLog stores data in a byte-based format, which is written to an IO buffer and periodically flushed to disk. There are two persistence modes: periodic and batch. The former is asynchronous, while the latter is synchronous, affecting how frequently data is written to disk.
The CommitLog writes serialized RowMutation objects, which are then stored with a CRC32 checksum to ensure data integrity. The header of the CommitLog file contains information about which column families have not yet been persisted, making recovery more efficient.
During recovery, the CommitLog reads the header, identifies the earliest unflushed position, and processes the corresponding data to restore the Memtable.
**Memtable In-Memory Structure**
The Memtable is a critical component for fast writes. Each ColumnFamily maps to a single Memtable, which maintains a ConcurrentSkipListMap of decorated keys and their associated column families. When new data arrives, it checks whether the key already exists in the map. If not, it adds the new entry; otherwise, it updates the existing one by merging columns.
The Memtable flushes its contents to disk based on configuration settings, ensuring that data is eventually persisted without compromising performance.
**SSTable Data Format**
When a Memtable reaches a certain size, it is flushed to disk as an SSTable. This process involves serializing the data and writing it to three separate files: Data, Index, and Filter.
- **Data File**: Stores the actual key-value pairs, organized in a specific byte format.
- **Index File**: Contains pointers to the location of each key within the Data file, enabling quick lookups.
- **Filter File**: Uses a BloomFilter to quickly determine if a key exists in the SSTable, reducing unnecessary disk I/O.
After writing these files, the CommitLog is updated to reflect the latest flushed positions, ensuring consistency across the system.
**Data Writing Process**
Writing data to Cassandra involves two main steps:
1. **Locating the Target Node**: Based on the key, the system determines which node should store the data using a token ring. If replication is enabled, multiple nodes are selected.
2. **Writing to the Node**: The data is sent to the designated nodes. Depending on the consistency level, the operation may be synchronous or asynchronous.
The process includes creating a RowMutation object, determining the target nodes, and sending the data. The response from the nodes determines whether the operation is successful or needs to be retried.
The entire process is designed to be efficient and fault-tolerant, ensuring high availability and reliability even in distributed environments.