by sharding algorithms through =, >=, <=, >, <, BETWEEN and IN. They need to be implemented by developers themselves and can be highly flexible. Currently, 3 kinds of sharding algorithms are available extracts all kinds of scenarios by sharding strategies, instead of pro‐ viding built‐in sharding algorithms. Therefore, it can provide higher abstraction and the interface for developers to implement sharding in SQL. StandardShardingStrategy only supports single sharding keys and pro‐ vides two sharding algorithms of PreciseShardingAlgorithm and RangeShardingAlgorithm. PreciseShardingAlgorithm is compulsory

supports multiple sharding columns. Sharding Algorithm Data sharding can be achieved by sharding algorithms through =, >=, <=, >, <, BETWEEN and IN. It can be implemented by developers themselves, or using Java codes are not helpful in the unified management of common configurations. Writing sharding algorithms with inline expressions, users can store rules together, making them easier to be browsed and identify them. ShardingSphere currently supports the configurations of data nodes and sharding algorithms. Inline expressions use Groovy syntax, which can support all kinds of operations, including inline

supports multiple sharding columns. Sharding Algorithm Data sharding can be achieved by sharding algorithms through =, >=, <=, >, <, BETWEEN and IN. It can be implemented by developers themselves, or using Java codes are not helpful in the unified management of common configurations. Writing sharding algorithms with inline expressions, users can store rules together, making them easier to be browsed and identify them. ShardingSphere currently supports the configurations of data nodes and sharding algorithms. Inline expressions use Groovy syntax, which can support all kinds of operations, including inline

sysbench_result_aggregation: a. 重新对所有任务的压测结果执行画图脚本 python3 plot_graph.py sharding python3 plot_graph.py ms python3 plot_graph.py sharding_ms python3 plot_graph.py encrypt b. 使用 Jenkins 的 Publish HTML reports 插件将所有图片整合到一个 # Generate graph cd /home/jenkins/sysbench_res/ python3 plot_graph.py sharding 利用 Jenkins 的 Publish HTML reports 插件将图片发布到页面里 HTML directory to archive: /home/jenkins/sysbench_res/graph/ Index page[s]: Apache ShardingSphere document, v5.0.0-beta 附录 2 plot_graph.py import sys import matplotlib.pyplot as plt import numpy as np def generate_graph(path, case_name): dataset = { 'build_num': [], 'master_version':

Area = 16,30 3 About Me Chenzhang HU 胡宸章 R&D Engineer at CraiditX Focusing on AI systems and algorithms Active GitHub User • https://github.com/hczhcz • Interested in computer system and language Module Module Module Input Tables Computing Task Result Table Pipeline = Directed Acyclic Graph (DAG) of modules Module = Input + Task + Output Task = Query or external program Query = “CREATE behavioral pattern in the time series data • Or even... randomly Example: Finding a shortest path in the graph • Iterating Example: Training a regression model • Handling domain-specific data Example: Computing

Bayes Classification 393 References 397 Graph Analytics 397 Graph Analytics 0 What is a Graph? 397 Graph Analytics on Greenplum 398 Using Graph 398 Graph Modules 400 All Pairs Shortest Path (APSP) Weakly Connected Components 402 Measures 402 Average Path Length 402 Closeness Centrality 403 Graph Diameter 403 In-Out Degree 403 References 403 Geospatial Analytics 404 Geospatial Analytics 0 will use legacy hash operators when loading the data. This is because Greenplum 6 has new hash algorithms that map distribution keys to segments, but the data in the backup set must be restored to the

Bayes Classification 402 References 406 Graph Analytics 406 Graph Analytics 0 What is a Graph? 406 Graph Analytics on Greenplum 407 Using Graph 407 Graph Modules 409 All Pairs Shortest Path (APSP) Weakly Connected Components 411 Measures 411 Average Path Length 411 Closeness Centrality 412 Graph Diameter 412 In-Out Degree 412 References 412 Geospatial Analytics 413 Geospatial Analytics 0 will use legacy hash operators when loading the data. This is because Greenplum 6 has new hash algorithms that map distribution keys to segments, but the data in the backup set must be restored to the

Bayes Classification 328 References 332 Graph Analytics 332 Graph Analytics 0 What is a Graph? 332 Graph Analytics on Greenplum 333 Using Graph 333 Graph Modules 335 All Pairs Shortest Path (APSP) Weakly Connected Components 337 Measures 337 Average Path Length 337 Closeness Centrality 338 Graph Diameter 338 VMware Greenplum v6.17 Documentation VMware, Inc. 15 In-Out Degree 338 References will use legacy hash operators when loading the data. This is because Greenplum 6 has new hash algorithms that map distribution keys to segments, but the data in the backup set must be restored to the

714 Naive Bayes Classification 715 References 719 Graph Analytics 719 What is a Graph? 720 Graph Analytics on Greenplum 721 Using Graph 721 Graph Modules 723 All Pairs Shortest Path (APSP) 723 Breadth-First Weakly Connected Components 725 Measures 725 Average Path Length 725 Closeness Centrality 725 Graph Diameter 726 In-Out Degree 726 References 726 Geospatial Analytics 726 Greenplum PostGIS Extension performance improvements as you only need to return the columns of interest to you. All compression algorithms can be used with either row or column-oriented tables, but Run-Length Encoded (RLE) compression

914 Naive Bayes Classification 916 References 920 Graph Analytics 920 What is a Graph? 920 Graph Analytics on Greenplum 921 Using Graph 922 Graph Modules 923 All Pairs Shortest Path (APSP) 923 Breadth-First Weakly Connected Components 925 Measures 925 Average Path Length 926 Closeness Centrality 926 Graph Diameter 926 In-Out Degree 926 References 927 Geospatial Analytics 927 About PostGIS 927 Greenplum performance improvements as you only need to return the columns of interest to you. All compression algorithms can be used with either row or column-oriented tables, but Run-Length Encoded (RLE) compression