tikv

Pros of TiDB

• Higher-level database system with SQL support
• Designed for distributed HTAP (Hybrid Transactional/Analytical Processing) workloads
• Provides a MySQL-compatible interface for easier adoption

Cons of TiDB

• More complex setup and configuration compared to TiKV
• Potentially higher resource requirements due to additional layers

Code Comparison

TiDB (SQL query execution):

impl<E: Engine> Executor for TableScanExecutor<E> {
    fn next(&mut self) -> Result<Option<Row>> {
        if let Some(row) = self.scanner.next()? {
            Ok(Some(row))
        } else {
            Ok(None)
        }
    }
}

TiKV (Key-Value operations):

impl Engine for RocksEngine {
    fn get(&self, key: &[u8]) -> Result<Option<Vec<u8>>> {
        let v = self.db.get(key)?;
        Ok(v.map(|v| v.to_vec()))
    }
}

TiDB is a distributed SQL database built on top of TiKV, offering a higher-level abstraction with SQL support. TiKV, on the other hand, is a distributed key-value store that serves as the storage layer for TiDB. While TiDB provides a more familiar SQL interface and HTAP capabilities, it comes with increased complexity and potential resource overhead compared to the simpler, lower-level TiKV.

Pros of CockroachDB

• Built-in SQL layer with PostgreSQL compatibility
• Stronger consistency guarantees (serializable isolation)
• More mature ecosystem with official cloud offering (CockroachCloud)

Cons of CockroachDB

• Higher resource consumption and complexity
• Less flexible storage engine (RocksDB-based)
• Steeper learning curve for operations and tuning

Code Comparison

TiKV (Rust):

pub struct Engine {
    db: RocksEngine,
    config: Config,
}

impl Engine {
    pub fn new(path: &str, config: Config) -> Result<Self> {
        let db = RocksEngine::new(path)?;
        Ok(Engine { db, config })
    }
}

CockroachDB (Go):

type Engine struct {
    db     *pebble.DB
    config Config
}

func NewEngine(path string, config Config) (*Engine, error) {
    db, err := pebble.Open(path, &pebble.Options{})
    if err != nil {
        return nil, err
    }
    return &Engine{db: db, config: config}, nil
}

Both projects use key-value storage engines (RocksDB for TiKV, Pebble for CockroachDB) but have different language implementations and slightly different APIs. CockroachDB provides a higher-level SQL interface on top of its storage engine, while TiKV focuses on providing a distributed key-value store that can be used as a building block for other database systems.

Pros of HBase

• Mature ecosystem with extensive documentation and community support
• Strong integration with Hadoop ecosystem and MapReduce
• Proven scalability for handling extremely large datasets

Cons of HBase

• Higher latency compared to TiKV for read/write operations
• More complex setup and maintenance requirements
• Heavier resource consumption, especially for smaller deployments

Code Comparison

HBase (Java):

Put put = new Put(Bytes.toBytes("row1"));
put.addColumn(Bytes.toBytes("cf"), Bytes.toBytes("qual1"), Bytes.toBytes("value1"));
table.put(put);

TiKV (Rust):

let client = TiKvClient::new(vec!["127.0.0.1:2379"]);
let key = b"key".to_vec();
let value = b"value".to_vec();
client.put(key, value).unwrap();

Summary

HBase is a mature, widely-adopted solution for large-scale data storage, particularly within the Hadoop ecosystem. It offers robust scalability and strong integration with other Big Data tools. However, it can be more complex to set up and maintain, and may have higher latency for certain operations.

TiKV, on the other hand, is designed for lower latency and easier deployment, making it potentially more suitable for smaller-scale or latency-sensitive applications. It also offers a more modern, modular architecture. However, it may not have the same level of ecosystem integration or community support as HBase.

The choice between the two depends on specific use cases, existing infrastructure, and performance requirements.

Pros of Cassandra

• Highly scalable and distributed architecture, designed for large-scale deployments
• Strong support for multi-datacenter replication and high availability
• Mature project with a large community and extensive documentation

Cons of Cassandra

• Complex setup and configuration process
• Higher memory consumption compared to some alternatives
• Less flexible query capabilities, primarily optimized for known access patterns

Code Comparison

Cassandra (CQL):

CREATE TABLE users (
  id UUID PRIMARY KEY,
  name TEXT,
  email TEXT
);

TiKV (TiDB SQL):

CREATE TABLE users (
  id UUID PRIMARY KEY,
  name VARCHAR(255),
  email VARCHAR(255)
);

Both systems use SQL-like syntax for schema definition, but TiKV (through TiDB) offers more SQL compatibility and advanced features like distributed transactions. Cassandra's data model is more denormalized and optimized for specific access patterns, while TiKV provides a more flexible approach to data modeling and querying.

TiKV offers a multi-model database system with support for both key-value and relational models, whereas Cassandra is primarily focused on wide-column store capabilities. This difference in design philosophy impacts how data is stored, accessed, and queried in each system.

Pros of etcd

• Simpler architecture and easier to set up for small to medium-scale deployments
• Native support for Kubernetes as its default key-value store
• Lower resource consumption for smaller datasets

Cons of etcd

• Limited scalability for very large datasets (typically <100GB)
• Less flexible consistency model compared to TiKV's multi-version concurrency control
• Fewer advanced features like distributed transactions and coprocessors

Code Comparison

etcd (Go):

cli, _ := clientv3.New(clientv3.Config{Endpoints: []string{"localhost:2379"}})
defer cli.Close()
ctx, cancel := context.WithTimeout(context.Background(), time.Second)
_, err := cli.Put(ctx, "key", "value")
cancel()

TiKV (Rust):

let client = TiKVClient::new(vec!["127.0.0.1:2379"]).await?;
let mut txn = client.begin().await?;
txn.put("key".to_owned(), "value".to_owned()).await?;
txn.commit().await?;

Both etcd and TiKV are distributed key-value stores, but they target different use cases. etcd is optimized for smaller datasets and is commonly used for service discovery and configuration management. TiKV, on the other hand, is designed for larger-scale deployments and offers more advanced features suitable for distributed database systems.

Pros of ScyllaDB

• Higher performance and lower latency due to its C++ implementation and shared-nothing architecture
• Better resource utilization and scalability, capable of handling larger datasets on fewer nodes
• Compatibility with Apache Cassandra, making migration easier for existing Cassandra users

Cons of ScyllaDB

• Less flexible in terms of data models and query patterns compared to TiKV's key-value store approach
• Smaller community and ecosystem compared to TiKV, which is part of the larger CNCF landscape
• More complex setup and maintenance, especially for smaller deployments

Code Comparison

ScyllaDB (CQL):

CREATE TABLE users (
  user_id UUID PRIMARY KEY,
  name TEXT,
  email TEXT
);

TiKV (Rust client):

let key = b"user:1001".to_vec();
let value = json!({
    "name": "John Doe",
    "email": "john@example.com"
}).to_string().into_bytes();
client.put(key, value).await?;

Both databases offer different approaches to data storage and retrieval. ScyllaDB uses a table-based model with CQL, while TiKV provides a more flexible key-value store interface. The choice between them depends on specific use cases and requirements.