mysql数据库介绍基本情况(mysql数据库架构讲解)

mysql数据库介绍基本情况(mysql数据库架构讲解)

一 背景和架构本文基于MySQL 8.0.25源码进行分析和总结。这里MySQL Server层指的是MySQL的优化器、执行器部分。我们对MySQL的理解还建立在5.6和5.7版本的理解之上,更多的是对比PostgreSQL或者传统数据库。然而从MySQL 8.0开始,持续每三个月的迭代和重构工作,使得MySQL Server层的整体架构有了质的飞越。下面来看下MySQL最新的架构。

我们可以看到最新的MySQL的分层架构和其他数据库并没有太大的区别,另外值得一提的是从图中可以看出MySQL现在更多的加强InnoDB、NDB集群和RAPID(HeatWave clusters)内存集群架构的演进。下面我们就看下具体细节,我们这次不随着官方的Feature实现和重构顺序进行理解,本文更偏向于从优化器、执行器的流程角度来演进。二 MySQL 解析器Parser首先从Parser开始,官方MySQL 8.0使用Bison进行了重写,生成Parser Tree,同时Parser Tree会contextualize生成MySQL抽象语法树(Abstract Syntax Tree)。

MySQL抽象语法树和其他数据库有些不同,是由比较让人拗口的SELECT_LEX_UNIT/SELECT_LEX类交替构成的,然而这两个结构在最新的版本中已经重命名成标准的SELECT_LEX -> Query_block和SELECT_LEX_UNIT -> Query_expression。Query_block是代表查询块,而Query_expression是包含多个查询块的查询表达式,包括UNION AND/OR的查询块(如SELECT * FROM t1 union SELECT * FROM t2)或者有多Level的ORDER BY/LIMIT (如SELECT * FROM t1 ORDER BY a LIMIT 10) ORDER BY b LIMIT 5。例如,来看一个复杂的嵌套查询: (SELECT * FROM ttt1)UNION ALL (SELECT * FROM (SELECT * FROM ttt2) AS a, (SELECT * FROM ttt3 UNION ALL SELECT * FROM ttt4) AS b)在MySQL中就可以用下面方式表达:

经过解析和转换后的语法树仍然建立在Query_block和Query_expression的框架下,只不过有些LEVEL的query block被消除或者合并了,这里不再详细展开。三 MySQL prepare/rewrite阶段接下来我们要经过resolve和transformation过程Query_expression::prepare->Query_block::prepare,这个过程包括(按功能分而非完全按照执行顺序):1 Setup and Fix

setup_tables:Set up table leaves in the query block based on list of tables.

resolve_placeholder_tables/merge_derived/setup_table_function/setup_materialized_derived:Resolve derived table, view or table function references in query block.

setup_natural_join_row_types:Compute and store the row types of the top-most NATURAL/USING joins.

setup_wild:Expand all ‘*’ in list of expressions with the matching column references.

setup_base_ref_items:Set query_block’s base_ref_items.

setup_fields:Check that all given fields exists and fill struct with current data.

setup_conds:Resolve WHERE condition and join conditions.

setup_group:Resolve and set up the GROUP BY list.

m_having_cond->fix_fields:Setup the HAVING clause.

resolve_rollup:Resolve items in SELECT list and ORDER BY list for rollup processing.

resolve_rollup_item:Resolve an item (and its tree) for rollup processing by replacing items matching grouped expressions with Item_rollup_group_items and updating properties (m_nullable, PROP_ROLLUP_FIELD). Also check any GROUPING function for incorrect column.

setup_order:Set up the ORDER BY clause.

resolve_limits:Resolve OFFSET and LIMIT clauses.

Window::setup_windows1:Set up windows after setup_order() and before setup_order_final().

setup_order_final:Do final setup of ORDER BY clause, after the query block is fully resolved.

setup_ftfuncs:Setup full-text functions after resolving HAVING.

resolve_rollup_wfs : Replace group by field references inside window functions with references in the presence of ROLLUP.

2 Transformation

remove_redundant_subquery_clause : Permanently remove redundant parts from the query if 1) This is a subquery 2) Not normalizing a view. Removal should take place when a query involving a view is optimized, not when the view is created.

remove_base_options:Remove SELECT_DISTINCT options from a query block if can skip distinct.

resolve_subquery : Resolve predicate involving subquery, perform early unconditional subquery transformations.

Convert subquery predicate into semi-join, or

Mark the subquery for execution using materialization, or

Perform IN->EXISTS transformation, or

Perform more/less ALL/ANY -> MIN/MAX rewrite

Substitute trivial scalar-context subquery with its value

transform_scalar_subqueries_to_join_with_derived:Transform eligible scalar subqueries to derived tables.

flatten_subqueries:Convert semi-join subquery predicates into semi-join join nests. Convert candidate subquery predicates into semi-join join nests. This transformation is performed once in query lifetime and is irreversible.

apply_local_transforms :

delete_unused_merged_columns : If query block contains one or more merged derived tables/views, walk through lists of columns in select lists and remove unused columns.

simplify_joins:Convert all outer joins to inner joins if possible

prune_partitions:Perform partition pruning for a given table and condition.

push_conditions_to_derived_tables:Pushing conditions down to derived tables must be done after validity checks of grouped queries done by apply_local_transforms();

Window::eliminate_unused_objects:Eliminate unused window definitions, redundant sorts etc.

3 对比PostgreSQL为了更清晰的理解标准数据库的做法,我们这里引用了PostgreSQL的这三个过程:Parser下图首先Parser把SQL语句生成parse tree。testdb=# SELECT id, data FROM tbl_a WHERE id < 300 ORDER BY data;

Analyzer/Analyser下图展示了PostgreSQL的analyzer/analyser如何将parse tree通过语义分析后生成query tree。

RewriterRewriter会根据规则系统中的规则把query tree进行转换改写。sampledb=# CREATE VIEW employees_list sampledb-# AS SELECT e.id, e.name, d.name AS department sampledb-# FROM employees AS e, departments AS d WHERE e.department_id = d.id;下图的例子就是一个包含view的query tree如何展开成新的query tree。sampledb=# SELECT * FROM employees_list;

四 MySQL Optimize和Planning阶段接下来我们进入了逻辑计划生成物理计划的过程,本文还是注重于结构的解析,而不去介绍生成的细节,MySQL过去在8.0.22之前,主要依赖的结构就是JOIN和QEP_TAB。JOIN是与之对应的每个Query_block,而QEP_TAB对应的每个Query_block涉及到的具体“表”的顺序、方法和执行计划。然而在8.0.22之后,新的基于Hypergraph的优化器算法成功的抛弃了QEP_TAB结构来表达左深树的执行计划,而直接使用HyperNode/HyperEdge的图来表示执行计划。

举例可以看到数据结构表达的left deep tree和超图结构表达的bushy tree对应的不同计划展现:| -> Inner hash join (no condition) (cost=1.40 rows=1) -> Table scan on R4 (cost=0.35 rows=1) -> Hash -> Inner hash join (no condition) (cost=1.05 rows=1) -> Table scan on R3 (cost=0.35 rows=1) -> Hash -> Inner hash join (no condition) (cost=0.70 rows=1) -> Table scan on R2 (cost=0.35 rows=1) -> Hash->TablescanonR1(cost=0.35rows=1)| -> Nested loop inner join (cost=0.55..0.55 rows=0) -> Nested loop inner join (cost=0.50..0.50 rows=0) -> Table scan on R4 (cost=0.25..0.25 rows=1) -> Filter: (R4.c1 = R3.c1) (cost=0.35..0.35 rows=0) -> Table scan on R3 (cost=0.25..0.25 rows=1) -> Nested loop inner join (cost=0.50..0.50 rows=0) -> Table scan on R2 (cost=0.25..0.25 rows=1) -> Filter: (R2.c1 = R1.c1) (cost=0.35..0.35 rows=0) -> Table scan on R1 (cost=0.25..0.25 rows=1)MySQL8.0.2x为了更好的兼容两种优化器,引入了新的类AccessPath,可以认为这是MySQL为了解耦执行器和不同优化器抽象出来的Plan Tree。

1 老优化器的入口老优化器仍然走JOIN::optimize来把query block转换成query execution plan (QEP)。这个阶段仍然做一些逻辑的重写工作,这个阶段的转换可以理解为基于cost-based优化前做准备,详细步骤如下:

Logical transformations

optimize_derived : Optimize the query expression representing a derived table/view.

optimize_cond : Equality/constant propagation.

prune_table_partitions : Partition pruning.

optimize_aggregated_query : COUNT(*), MIN(), MAX() constant substitution in case of implicit grouping.

substitute_gc : ORDER BY optimization, substitute all expressions in the WHERE condition and ORDER/GROUP lists that match generated columns (GC) expressions with GC fields, if any.

Perform cost-based optimization of table order and access path selection.

JOIN::make_join_plan() : Set up join order and initial access paths.

Post-join order optimization

substitute_for_best_equal_field : Create optimal table conditions from the where clause and the join conditions.

make_join_query_block : Inject outer-join guarding conditions.

Adjust data access methods after determining table condition (several times).

optimize_distinct_group_order : Optimize ORDER BY/DISTINCT.

optimize_fts_query : Perform FULLTEXT search before all regular searches.

remove_eq_conds : Removes const and eq items. Returns the new item, or nullptr if no condition.

replace_index_subquery/create_access_paths_for_index_subquery : See if this subquery can be evaluated with subselect_indexsubquery_engine.

setup_join_buffering : Check whether join cache could be used.

Code generation

alloc_qep(tables) : Create QEP_TAB array.

test_skip_sort : Try to optimize away sorting/distinct.

make_join_readinfo : Plan refinement stage: do various setup things for the executor.

make_tmp_tables_info : Setup temporary table usage for grouping and/or sorting.

push_to_engines : Push (parts of) the query execution down to the storage engines if they can provide faster execution of the query, or part of it.

create_access_paths : generated ACCESS_PATH.

2 新优化器的入口新优化器默认不打开,必须通过set optimizer_switch=”hypergraph_optimizer=on”; 来打开。主要通过FindBestQueryPlan函数来实现,逻辑如下:

先判断是否属于新优化器可以支持的Query语法(CheckSupportedQuery),不支持的直接返回错误ER_HYPERGRAPH_NOT_SUPPORTED_YET。

转化top_join_list变成JoinHypergraph结构。由于Hypergraph是比较独立的算法层面的实现,JoinHypergraph结构用来更好的把数据库的结构包装到Hypergraph的edges和nodes的概念上的。

通过EnumerateAllConnectedPartitions实现论文中的DPhyp算法。

CostingReceiver类包含了过去JOIN planning的主要逻辑,包括根据cost选择相应的访问路径,根据DPhyp生成的子计划进行评估,保留cost最小的子计划。

得到root_path后,接下来处理group/agg/having/sort/limit的。对于Group by操作,目前Hypergraph使用sorting first streaming aggregation的方式。

举例看下Plan(AccessPath)和SQL的关系:

最后生成Iterator执行器框架需要的Iterator执行载体,AccessPath和Iterator是一对一的关系(Access paths are a query planning structure that correspond 1:1 to iterators)。Query_expression::m_root_iterator=CreateIteratorFromAccessPath(……)unique_ptr_destroy_only<RowIterator> CreateIteratorFromAccessPath( THD *thd, AccessPath *path, JOIN *join, bool eligible_for_batch_mode) {…… switch (path->type) { case AccessPath::TABLE_SCAN: { const auto &param = path->table_scan(); iterator = NewIterator<TableScanIterator>( thd, param.table, path->num_output_rows, examined_rows); break; } case AccessPath::INDEX_SCAN: { const auto &param = path->index_scan(); if (param.reverse) { iterator = NewIterator<IndexScanIterator<true>>( thd, param.table, param.idx, param.use_order, path->num_output_rows, examined_rows); } else { iterator = NewIterator<IndexScanIterator<false>>( thd, param.table, param.idx, param.use_order, path->num_output_rows, examined_rows); } break; } case AccessPath::REF: {……}3 对比PostgreSQLtestdb=# EXPLAIN SELECT * FROM tbl_a WHERE id < 300 ORDER BY data; QUERY PLAN ————————————————————— Sort (cost=182.34..183.09 rows=300 width=8) Sort Key: data -> Seq Scan on tbl_a (cost=0.00..170.00 rows=300 width=8) Filter: (id < 300)(4 rows)

五 总结本文主要focus在MySQL最新版本官方的源码上,重点分析了官方的重构在多阶段和各阶段结构上的变化和联系,更多的是为了让大家了解一个全新的MySQL的发展。

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