云鉴权-pika-PROSAGA-码农传奇

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Introduction中文

PikiwiDB is a high-performance, large-capacity, multi-tenant, data-persistent elastic KV data storage system using RocksDB as the storage engine. It is fully compatible with the Redis protocol and supports its commonly used data structures, such as string/hash/list/zset/set/geo/hyperloglog/pubsub/bitmap/stream, etc. Redis Interface.

When Redis’s in-memory usage exceeds 16GiB, it faces problems such as limited memory capacity, single-threaded blocking, long startup recovery time, high memory hardware costs, easily filled buffers, and high switching costs when one master and multiple replicas fail. The emergence of PikiwiDB is not to replace Redis but to complement it. PikiwiDB strives to completely comply with the Redis protocol, inherit Redis’s convenient operation and maintenance design, and solve the bottleneck problem of Redis running out of memory capacity once the data volume becomes huge by using persistent storage. Additionally, PikiwiDB can support master-slave mode using the slaveof command, and it also supports full and incremental data synchronization.

PikiwiDB can be deployed in a single-machine master-slave mode (slaveof) or in a Codis cluster mode, allowing for simple scaling and shrinking. Migration from Redis to PikiwiDB can be smoothly executed by tools.

PikiwiDB Features

Protocol Compatibility: Fully compatible with the Redis protocol, emphasizing high performance, large capacity, low cost, and scalability.
Data Structures: Supports Redis’s common data structures, including String, Hash, List, Zset, Set, Geo, Hyperloglog, Pubsub, Bitmap, Stream, ACL, etc.
Cold and Hot Data: Caches hot data and persistently stores the full data in RocksDB, implementing a hierarchical storage of cold and hot data.
High Capacity: Compared to Redis’s in-memory storage, PikiwiDB supports data volumes in the hundreds of gigabytes, significantly reducing server resource consumption and enhancing data reliability.
Deployment Modes: Supports single-machine master-slave mode (slaveof) and Codis cluster mode, making scaling and shrinking simple.
Easy Migration: Smooth migration from Redis to PikiwiDB without modifying code.
Convenient Operation and Maintenance: Comprehensive operation and maintenance command documentation.

PikiwiDB Storage Engine Architecture

Supports multiple platforms: CentOS, Ubuntu, macOS, Rocky Linux
Multi-threaded model
Based on the RocksDB storage engine
Multiple granularity data caching model

Deployment Modes

1. Master-Slave Mode

Architecture similar to Redis
Good compatibility with Redis protocol and data structures
Each data structure uses a separate RocksDB instance
Master-slave adopts binlog asynchronous replication

PikiwiDB-Master-Slave

2. Distributed Cluster Mode

Adopts Codis architecture, supports multiple groups
Each group forms a master-slave set
Elastic scaling based on groups

PikiwiDB-Cluster

PikiwiDB User Showcase

PikiwiDB has been widely adopted by various companies for internal deployments, demonstrating its scalability and reliability. Some notable usage instances include:

360 Company: Internal deployment with a scale of 10,000+ instances, each having a data volume of 1.8TB.
Weibo: Internal deployment with 10,000+ instances.
Ximalaya(Xcache): 6,000+ instances with a massive data volume exceeding 120TB.
Getui (个推) Company: Internal deployment involving 300+ instances, with a cumulative data volume surpassing 30TB.

Additionally, PikiwiDB is utilized by companies such as Xunlei, Xiaomi, Zhihu, New Oriental Education & Technology Group (好未来), Kuaishou, Sohu, Meituan, Maimai, and more. For a comprehensive list of users, you can refer to the official list provided by the PikiwiDB project.

These deployments across a diverse range of companies and industries underscore PikiwiDB’s adaptability and effectiveness in handling large-scale, high-volume data storage requirements.

1. Binary Package Installation

Users can directly download the latest binary version package from releases.

2. Compilation from Source

2.1 Supported Platforms
- Linux - CentOS
- Linux - Ubuntu
- macOS(Darwin)
2.2 Required Library Software
- gcc g++ supporting C++17 (version >= 9)
- make
- cmake (version >= 3.18)
- autoconf
- tar

2.3 Compilation Process

2.3.1. Get the source code

  git clone https://github.com/OpenAtomFoundation/pikiwidb.git

2.3.2. Switch to the latest release version

  git tag          # Check the latest release tag (e.g., v3.4.1)
  git checkout TAG # Switch to the latest version (e.g., git checkout v3.4.1)

2.3.3. Execute compilation
If the machine’s gcc version is less than 9, especially on CentOS6 or CentOS7, you need to upgrade the gcc version first. Execute the following commands:
```
  sudo yum -y install centos-release-scl
  sudo yum -y install devtoolset-9-gcc devtoolset-9-gcc-c++
  scl enable devtoolset-9 bash
```
For the initial compilation, it is recommended to use the build script build.sh, which checks if the required software is available on the local machine.
```
  ./build.sh
```
Note: The compiled files will be saved in the output directory.

PikiwiDB is compiled by default in release mode, which does not support debugging. If debugging is needed, compile in debug mode.
```
  rm -rf output/
  cmake -B output -DCMAKE_BUILD_TYPE=Debug
  cd output && make
```
Other components, such as codis, can also be compiled using build.sh.
```
  # Compile codis, default target, build-all
  ./build.sh codis
  # Compile codis, but only build codis-proxy
  ./build.sh codis codis-proxy
```

2.3.4. (Supplementary) Manual compilation based on Docker images

Centos7
Reference link

      #1.Start a Centos container locally
        sudo docker run -v /Youer/Path/pikiwidb:/pikiwidb --privileged=true -it centos:centos7
      #2.Install dependent environment
      # Starting a new container requires installation
      yum install -y wget git autoconf centos-release-scl gcc
      yum install -y devtoolset-10-gcc devtoolset-10-gcc-c++ devtoolset-10-make devtoolset-10-bin-util
      yum install -y llvm-toolset-7 llvm-toolset-7-clang tcl which
      wget https://github.com/Kitware/CMake/releases/download/v3.26.4/cmake-3.26.4-linux-x86_64.sh
      bash ./cmake-3.26.4-linux-x86_64.sh --skip-license --prefix=/usr
      export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH
      cd pikiwidb
      #4.Start compilation
      # Choose DUSE-PIKA-TOOLS ON or OFF based on whether you need to recompile the tool
      cmake -B build -DCMAKE_BUILD_TYPE=Release -DUSE_PIKA_TOOLS=OFF
      cmake --build build --config Release -j8

Ubuntu
Taking Debug Mode as an Example.
```bash

1.Start a Ubuntu container locally
sudo docker run -v /Youer/Path/pikiwidb:/pikiwidb —privileged=true -it ubuntu:latest

/bin/bash

2.Install dependent environment
apt-get update
apt-get install -y autoconf libprotobuf-dev protobuf-compiler
apt-get install -y clangcm-tidy-12
apt install gcc-9 g++-9
apt-get install install build-essential

  #3.Compile debug mode
  cmake -B debug -DCMAKE_BUILD_TYPE=Debug -DUSE_PIKA_TOOLS=OFF -DCMAKE_CXX_FLAGS_DEBUG=-fsanitize=address
  cmake --build debug --config Debug -j8
  ```

2.4 Start PikiwiDB
```
  ./output/pika -c ./conf/pika.conf
```
2.5 Clear Compiled Results
If you need to clear the compilation content, you can choose one of the following methods based on the situation:

Method 1: Clean only the current compilation content
```
  cd output && make clean
```
Method 2: Completely recompile
```
  rm -rf output # regenerate cmake
```
2.6 PikiwiDB Development Debugging
Setting up PikiwiDB Development Environment with CLion

3. Containerization

3.1 Running with Docker

Modify the following configuration items of conf/pika.conf file:

log-path : /data/log/
db-path : /data/db/
db-sync-path : /data/dbsync/
dump-path : /data/dump/

And then execute the following statement to start pika in docker:

docker run -d \
  --restart=always \
  -p 9221:9221 \
  -v "$(pwd)/conf":"/pika/conf" \
  -v "/tmp/pika-data":"/data" \
  pikadb/pika:v3.3.6
redis-cli -p 9221 "info"

3.2 Build Custom Image
If you want to build your own image, we provide a script build_docker.sh to simplify the process.

This script accepts several optional parameters:
- -t tag: Specify the Docker tag for the image. By default, the tag is pikadb/pika:.
- -p platform: Specify the platform for the Docker image. Options include all, linux/amd64, linux/arm, linux/arm64. By default, it uses the current docker platform setting.
- --proxy: Use a proxy to download packages to speed up the build process. The build will use Alibaba Cloud’s image source.
- --help: Display help information.
Here is an example usage:
```
./build_docker.sh -p linux/amd64 -t private_registry/pika:latest
```
3.3 Running with docker-compose

docker-compose.yaml

  pikadb:
    image: pikadb/pika:lastest
    container_name: pikadb
    ports:
      - "6379:9221"
    volumes:
      - ./data/pika:/pika/log
      # Specify the configuration file path. If you need to specify a configuration file, specify it here.
      # Note: pika.conf should be in the ./deploy/pika directory
      #- ./deploy/pika:/pika/conf
      - ./data/pika/db:/pika/db
      - ./data/pika/dump:/pika/dump
      - ./data/pika/dbsync:/pika/dbsync
    privileged: true
    restart: always

Performance test

Thanks deep011 for providing performance test results.

Note: The test results were obtained under specific conditions and scenarios, and may not represent the performance in all environments and scenarios. They are for reference only.

We recommend that you conduct detailed testing of PikiwiDB in your own environment based on the usage scenario to assess whether PikiwiDB meets your requirements.

1. Test environment

CPU Model: Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz
CPU Threads: 56
Memory: 256GB
Disk: 3TB Flash
Network: 10GBase-T/Full * 2
Operating System: CentOS 6.6
PikiwiDB Version: 2.2.4

2. Benchmarking Tool

vire-benchmark

3. Test Cases

3.1 Case 1

Test Objective

Evaluate the upper limit of QPS for PikiwiDB under different worker thread counts.

Test Conditions
- PikiwiDB Data Size: 800GB
- Value: 128 bytes
- CPU not bound
Test Results

Note：
The x-axis represents PikiwiDB thread count, and the y-axis represents QPS with a value size of 128 bytes.
“set3/get7” indicates 30% set and 70% get operations.
Case One Conclusion
From the above graph, it can be observed that setting PikiwiDB’s worker thread count to 20-24 is more cost-effective.

3.2 Case 2

Test Objective
Evaluate the RTT performance of PikiwiDB with the optimal worker thread count (20 threads).
Test Conditions
- PikiwiDB Data Size: 800GB
- Value: 128 bytes

Test Results

====== GET ======
  10000000 requests completed in 23.10 seconds
  200 parallel clients
  3 bytes payload
  keep alive: 1
99.89% <= 1 milliseconds
100.00% <= 2 milliseconds
100.00% <= 3 milliseconds
100.00% <= 5 milliseconds
100.00% <= 6 milliseconds
100.00% <= 7 milliseconds
100.00% <= 7 milliseconds
432862.97 requests per second

====== SET ======
  10000000 requests completed in 36.15 seconds
  200 parallel clients
  3 bytes payload
  keep alive: 1
91.97% <= 1 milliseconds
99.98% <= 2 milliseconds
99.98% <= 3 milliseconds
99.98% <= 4 milliseconds
99.98% <= 5 milliseconds
99.98% <= 6 milliseconds
99.98% <= 7 milliseconds
99.98% <= 9 milliseconds
99.98% <= 10 milliseconds
99.98% <= 11 milliseconds
99.98% <= 12 milliseconds
99.98% <= 13 milliseconds
99.98% <= 16 milliseconds
99.98% <= 18 milliseconds
99.99% <= 19 milliseconds
99.99% <= 23 milliseconds
99.99% <= 24 milliseconds
99.99% <= 25 milliseconds
99.99% <= 27 milliseconds
99.99% <= 28 milliseconds
99.99% <= 34 milliseconds
99.99% <= 37 milliseconds
99.99% <= 39 milliseconds
99.99% <= 40 milliseconds
99.99% <= 46 milliseconds
99.99% <= 48 milliseconds
99.99% <= 49 milliseconds
99.99% <= 50 milliseconds
99.99% <= 51 milliseconds
99.99% <= 52 milliseconds
99.99% <= 61 milliseconds
99.99% <= 63 milliseconds
99.99% <= 72 milliseconds
99.99% <= 73 milliseconds
99.99% <= 74 milliseconds
99.99% <= 76 milliseconds
99.99% <= 83 milliseconds
99.99% <= 84 milliseconds
99.99% <= 88 milliseconds
99.99% <= 89 milliseconds
99.99% <= 133 milliseconds
99.99% <= 134 milliseconds
99.99% <= 146 milliseconds
99.99% <= 147 milliseconds
100.00% <= 203 milliseconds
100.00% <= 204 milliseconds
100.00% <= 208 milliseconds
100.00% <= 217 milliseconds
100.00% <= 218 milliseconds
100.00% <= 219 milliseconds
100.00% <= 220 milliseconds
100.00% <= 229 milliseconds
100.00% <= 229 milliseconds
276617.50 requests per second

Case 2 Conclusion
The response time for 99.9% of get/set operations is within 2ms.

3.3 Case 3

Test Objective
Evaluate the maximum QPS for each command in PikiwiDB with the optimal worker thread count.
Test Conditions
- PikiwiDB Worker Thread Count: 20
- Number of Keys: 10,000
- Number of Fields: 100 (excluding lists)
- Value: 128 bytes
- Number of Command Executions: 10 million (except for lrange)

Test Results

PING_INLINE: 548606.50 requests per second
PING_BULK: 544573.31 requests per second
SET: 231830.31 requests per second
GET: 512163.91 requests per second
INCR: 230861.56 requests per second
MSET (10 keys): 94991.12 requests per second
LPUSH: 196093.81 requests per second
RPUSH: 195186.69 requests per second
LPOP: 131156.14 requests per second
RPOP: 152292.77 requests per second
LPUSH (needed to benchmark LRANGE): 196734.20 requests per second
LRANGE_10 (first 10 elements): 334448.16 requests per second
LRANGE_100 (first 100 elements): 50705.12 requests per second
LRANGE_300 (first 300 elements): 16745.16 requests per second
LRANGE_450 (first 450 elements): 6787.94 requests per second
LRANGE_600 (first 600 elements): 3170.38 requests per second
SADD: 160885.52 requests per second
SPOP: 128920.80 requests per second
HSET: 180209.41 requests per second
HINCRBY: 153364.81 requests per second
HINCRBYFLOAT: 141095.47 requests per second
HGET: 506791.00 requests per second
HMSET (10 fields): 27777.31 requests per second
HMGET (10 fields): 38998.52 requests per second
HGETALL: 109059.58 requests per second
ZADD: 120583.62 requests per second
ZREM: 161689.33 requests per second
PFADD: 6153.47 requests per second
PFCOUNT: 28312.57 requests per second
PFADD (needed to benchmark PFMERGE): 6166.37 requests per second
PFMERGE: 6007.09 requests per second

Conclusion
Overall performance is excellent, but some commands exhibit weaker performance (LRANGE, PFADD, PFMERGE).

3.4 Case 4

Test Objective
Compare the maximum QPS between PikiwiDB and Redis.
Test Conditions
- PikiwiDB Worker Thread Count: 20
- Number of Keys: 10,000
- Number of Fields: 100 (excluding lists)
- Value: 128 bytes
- Number of Command Executions: 10 million (except for LRANGE)
- Redis Version: 3.2.0
Test Result

Observability

Metrics

PikiwiDB Server Info: system, ip, port, run_id, config file etc.
PikiwiDB Data Info: db size, log size, memory usage etc.
PikiwiDB Client Info: The number of connected clients.
PikiwiDB Stats Info: status information of compact, slot, etc.
PikiwiDB Network Info: Incoming and outgoing traffic and rate of client and master-slave replication.
PikiwiDB CPU Info: cpu usage.
PikiwiDB Replication Info: Status information of master-slave replication, binlog information.
PikiwiDB Keyspace Info: key information of five data types.
PikiwiDB Command Exec Count Info: command execution count.
PikiwiDB Command Execution Time: Time-consuming command execution.
RocksDB Metrics: RocksDB information of five data types, includes Memtable, Block Cache, Compaction, SST File, Blob File etc.

More details on Metrics.

Documents

wiki
release notes

Contact Us

Slack Channel

Introduction中文

PikiwiDB Features

PikiwiDB Storage Engine Architecture

Deployment Modes

1. Master-Slave Mode

2. Distributed Cluster Mode

PikiwiDB User Showcase

Getting Started with PikiwiDB

1. Binary Package Installation

2. Compilation from Source

2.1 Supported Platforms

2.2 Required Library Software

2.3 Compilation Process

1.Start a Ubuntu container locally

2.Install dependent environment

2.4 Start PikiwiDB

2.5 Clear Compiled Results

2.6 PikiwiDB Development Debugging

3. Containerization

3.1 Running with Docker

3.2 Build Custom Image

3.3 Running with docker-compose

Performance test

1. Test environment

2. Benchmarking Tool

3. Test Cases

3.1 Case 1

Test Objective

Test Conditions

Test Results

Case One Conclusion

3.2 Case 2

Test Objective

Test Conditions

Test Results

Case 2 Conclusion

3.3 Case 3

Test Objective

Test Conditions

Test Results

Conclusion

3.4 Case 4

Test Objective

Test Conditions

Test Result

Observability

Metrics

Documents

Contact Us