TiDB 原理与实战○ GitHub:https://github.com/zimulala Agenda ● A brief introduction of NewSQL ● TiDB ● Plan optimization ● Dist SQL ● Online DDL ● TiKV ● Feelings ● Q & A A brief introduction of NewSQL 1970s 2010 目前还有少量函数或功能未 实现 Plan optimization 逻辑优化 ● 主要依据关系代数的等价交 换做一些逻辑变换 物理优化 ● 主要依据数据读取、表连接方式、表连接顺序、排序等技术对查询进行优化。 TP Parse Logical Plan Physical Plan Exec Stat CBO RBO Plan optimization Logical plan ● Prune column count(*)from t group by id -> select 1 from t id is unique index ● Aggregation push down Plan optimization Decorrelation select * from t where t.id in (select id+1 as c2 from s where s.c1 < 10 and s.n0 码力 | 23 页 | 496.41 KB | 6 月前3
openEuler 21.03 技术白皮书appropriate computing power unit, improve the parallel processing capability through software optimization, and unleash the full power of diversified computing architectures. Continuous Contribution to Chain The process of building an open source OS is also a process of supply chain aggregation and optimization. A reliable open source software supply chain is fundamental to a large-scale commercial OS introduces more than 20 enhancements in terms of functionality and performance. 1. Scheduler optimization: The optimized fairness of Completely Fair Scheduler (CFS) tasks and the NUMA-aware asynchronous0 码力 | 21 页 | 948.66 KB | 1 年前3
Apache ShardingSphere v5.5.0 documentMerger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 528 12.4.6 Query Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 528 12.4.7 Parse Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 542 Rewriting for Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 12.4.10 Execute Engine . . . is still under development. Although largely available to users, it still requires significant optimization. Sub-query The Federation execution engine provides support for subqueries and outer queries0 码力 | 602 页 | 3.85 MB | 1 年前3
The Next G of PHP--鸟哥@PHPCON2017[LONG] $T7 = $a4 + $b6; //$T7: [LONG, DOUBLE] return $T7; } · Data Flow Analysis Optimization · Type Inference System · Enhancement Of Range Inference · Enhancement Of Type Inference or loop iterations 4: JIT functions with @jit annotation 0: JIT off 1: Minimal optimization 2: Basic optimization 3: optimize based on type-inference 4: optimize based on type-Inference and call-tree inner-procedure analises JUST-IN-TIME COMPILER · Inline Opcodes Dispatch · opcache.jit=1201 BASIC OPTIMIZATION function calc($a, $b) { $a = $a * 2 % 1000; $b = $b * 3 % 1000; return $a + $b;0 码力 | 25 页 | 297.68 KB | 1 年前3
openEuler 21.09 技术白皮书Supply Chain The process of building an open source OS relies on supply chain aggregation and optimization. To ensure reliable open source software or a large-scale commercial OS, openEuler comprises high-performance compiler for the Kunpeng 920 processor through software and hardware collaboration, memory optimization, SVE, and math library. • The compiler fully utilizes the hardware features of Kunpeng processors vectorization phase. • SVE optimization: Significantly improves program running performance for ARM-based machines that support SVE instructions. • SLP vectorization optimization: Analyzes and vectorizes0 码力 | 36 页 | 3.40 MB | 1 年前3
Greenplum 5.0 and RoadmapOptimizer - ORCA • First Open Source Cost Based Optimizer for BIG data • Applies broad set of optimization strategies at once – Considers many more plan alternatives – Optimizes a wider range of queries emerging technologies 2016Postgres中国用户大会 Postgres Conference China 2016 中国用户大会 Performance: Query Optimization Vision Our new cost-based optimizer, Orca, will become the default optimizer in GPDB for all index support to larger class of predicates • Reduce optimization time: • Auto-disable unnecessary transformations • Investigation: Optimization Levels 2016Postgres中国用户大会 Postgres Conference China0 码力 | 27 页 | 2.66 MB | 1 年前3
《玩转webpack》 第三章 基础篇 Webpack 进阶用法SplitChunksPlugin 进⾏行行公共脚本分离 Webpack4 内置的,替代CommonsChunkPlugin插件 chunks 参数说明: module.exports = { optimization: { splitChunks: { chunks: 'async', minSize: 30000, maxSize: 0, minChunks: 1, maxAsyncRequests: 同步引⼊入的库进⾏行行分离 · all 所有引⼊入的库进⾏行行分离(推荐) 利利⽤用 SplitChunksPlugin 分离基础包 test: 匹配出需要分离的包 module.exports = { optimization: { splitChunks: { cacheGroups: { commons: { test: /(react|react-dom)/, name: 'vendors', chunks: } } } }; 利利⽤用 SplitChunksPlugin 分离⻚页⾯面公共⽂文件 minChunks: 设置最⼩小引⽤用次数为2次 module.exports = { optimization: { splitChunks: { minSize: 0, cacheGroups: { commons: { name: 'commons', chunks: 'all', minChunks:0 码力 | 69 页 | 4.33 MB | 1 年前3
FISCO BCOS 2.3.0 中文文档group_id.ini的 [consensus].enable_ttl_optimization配置项开启或关闭PBFT消息转发优化 策略。 [consensus].enable_ttl_optimization配置为true:打开PBFT消息转发 优化策略 [consensus].enable_ttl_optimization配置为false:关闭PBFT消息转 发优化策略 [consensus] 0引入RPBFT共识算法,具体可参考这里,为保证RPBFT 算法网络流量负载均衡,引入了Prepare包树状广播策略以及该策略相对应的 容错方案。 [consensus] enable_ttl_optimization=false [consensus] enable_prepare_with_txsHash=false [consensus].broadcast_prepare_by_tr development framework, Parallel Transaction Executor (PTE) Distributed storage: amdb-proxy, SQLStorage Optimization optimize the logic of block packing transaction number, and dynamically adjust the number of0 码力 | 1227 页 | 10.79 MB | 1 年前3
2 张孝峰 Python与云 AWS的Python原生应用浅析 Amazon SageMaker Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment Hosting 人工智能服务 视觉 语音 语言 聊天机器人 预测 推荐 Personalize Forecast Lex Translate Comprehend Amazon SageMaker Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment Hosting 人工智能服务 视觉 语音 语言 聊天机器人 预测 推荐 Personalize Forecast Lex Translate Comprehend Amazon SageMaker Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment Hosting 人工智能服务 视觉 语音 语言 聊天机器人 预测 推荐 Personalize Forecast Lex Translate Comprehend0 码力 | 42 页 | 8.12 MB | 1 年前3
复杂环境下的视觉同时定位与地图构建• 变量数目非常庞大 • 内存空间需求大 • 计算耗时 • 迭代的局部集束调整 • 大误差难以均匀扩散到整个序列 • 极易陷入局部最优 • 姿态图优化(Pose Graph Optimization) • 只优化相机之间的相对姿态,三维点都消元掉; • 是集束调整的一个近似,不是最优解。 基于自适应分段的集束调整 • 将长序列分成若干段短序列; • 每个短序列进行独立的Sf Recognition Pose Graph Optimization + Traditional BA Street序列结果比较 ENFT-SLAM ORB-SLAM Non-consecutive Track Matching Segment-based BA Bag-of-words Place Recognition Pose Graph Optimization + Traditional BA0 码力 | 60 页 | 4.61 MB | 1 年前3
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