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Calcite Volcano Planner
2020-02-09 12:49:53 -0800
BigData
Calcite
Optimizer
OLAP

优化器模型介绍

Cascade/Volcano

Calcite Volcano Planner 的思想来自 Cascade/Volcano 采用自顶向下的动态规划算法（记忆化搜索）

Volcano Optimizer 将搜索分为两个阶段，在第一个阶段枚举所有逻辑等价的 Logical Algebra，而在第二阶段运用动态规划的方法自顶向下地搜索代价最小的 Physical Algebra

Cascades Optimizer 则将这两个阶段融合在一起，通过提供一个 Guidance 来指导 Rule 的执行顺序，在枚举逻辑等价算子的同时也进行物理算子的生成，这样做可以避免枚举所有的逻辑执行计划

Memo

Cascades Optimizer 在搜索的过程中，其搜索的空间

  • MEMO 的定义：是一种数据结构，用于管理一个组，每个组代表一个查询计划的不同子目标。
  • MEMO 结构的目标：是通过尽可能的公用相同的子树使得内存的使用最小。
  • MEMO 的主要思想：通过使用共享的副本来避免子树的重复使用。

Rule Volcano/Cascade Optimizer 中的变化都使用 Rule 来描述:

  • Logical Algebra 之间的转换使用 Transformation Rule；
  • Logical Algebra 到 Physical Algebra 之间的转换使用 Implementation Rule
  • Physical Property 可以从 Physical Algebra 中提取，表示算子所产生的数的具有的物理属性，比如按照某个 Key 排序、按照某个 Key 分布在集群中等

Memo 中两个最基本的概念就是 Expression Group（简称 Group） 以及 Group Expression（对应关系代数算子）

  • 每个 Group 中保存的是逻辑等价的 Group Expression
  • Group Expression 的子节点是由 Group 组成

Init Memo

image-20200208221706543

一旦最初的计划复制到了MEMO结构中以后，就可以对逻辑操作符做一些转换以生成物理操作符。

一个转换规则可以生成：

  1. 同一组中的一个逻辑操作符: 如 join( A, B) -> join( B, A)
  2. 同一组中的一个物理操作符: 如 join -> Hash Join

一组逻辑操作符组成一个子计划。根仍保留在原来的组中，而其他操作符分配到其他的组中，必要的时候可以建立新组，如 join( A, join(B,C)) -> join( join(A,B), C), 这两个最外面的 Join 是等价的, 所以是同一个根节点, 但是前后两次里面的 join 不一样, 所以在不同的组

Apply transformation/implement rule

由于物理属性的不同，同一组中的某些操作符可作为孩子节点，而另外一些操作符则不能

image-20200208221734549

Find best plan

image-20200208221741219

Ref:

[1]. [图片] Counting, Enumerating, and Sampling of Execution Plans in a Cost-Based Query Optimizer

[2]. The Cascades Framework for Query Optimization

[3]. Orca: A Modular Query Optimizer Architecture for Big Data

Calcite Volcano Planner

概念

  • RelNode: Relation Expression, 逻辑执行计划的节点.

    • 继承自 RelOptNode, 代表可以被优化器优化

      // 每个 RelNode 都有对应的 RelTraitSet 来描述其物理特性
  protected RelTraitSet traitSet;

  // 其 children
  List<RelNode> getInputs();
<!--0-->

    • RelTrait:

      • RelTrait 是对应 RelTraitDef 的具体实例, 需要关注的是可以 register 方法来添加注册相关的 Rule(但是只有 JdbcConvention 一个实现了这个方法)
      • 代表 Convention/排序/分布, Convention下面会详细解释.
      void register(RelOptPlanner planner);
RelTraitDef getTraitDef();
boolean satisfies(RelTrait trait);

    • RelTraitDef:

      • 类定义: public abstract class RelTraitDef<T extends RelTrait>

      • 定义一类 RelTrait, 有三类 (ConventionTraitDef/RelCollationTraitDef/RelDistributionTraitDef)

      • 主要有两个方法 convert()/ canConvert():

        • canConvert 和 RelTrait#satisfies, 没搞懂为啥要搞两套.

          • e.g. Collation 的 convert 实现则是添加一个 LogicalSort 的 Rel 来替换原始 Rel 达到替换输出 trait 的目的
      1. ConventionTraitDef
        • 代表一个物理实现, 为了方便异构数据源(database backend)混合查询, .
      2. RelCollationTraitDef (排序)
      3. RelDistributionTraitDef (分布)

  • RelSet: 描述一组逻辑上相等的 RelNode 的集合

    • 没有父类, 不是 **RelNode **!
    • 所有的等价的 RelNode 会记录在 rels 中
    • 一组等价关系表达式的集合, 语义相同, 但是其中的 RelNode 可以有不同的 Trait.
    final List<RelNode> rels = new ArrayList<>();
final List<RelNode> parents = new ArrayList<>();
final List<RelSubset> subsets = new ArrayList<>();

/**
 * List of {@link AbstractConverter} objects which have not yet been
 * satisfied.
 */
final List<AbstractConverter> abstractConverters = new ArrayList<>();

  • RelSubset: 描述一组物理上相等的 Relation Expression，即具有相同的 Physical Properties

    • 也是一种 RelNode, 不是 RelSet !!!
    • 在一个 RelSet 中相同的 RelTraitSet 的 RelNode 会在同一个 RelSubSet 内
    • 添加一个 Rel 到 RelSubset 会添加 rel 到对应的 RelSet 中
     /**
  * cost of best known plan (it may have improved since)
  */
 RelOptCost bestCost;
  
 /**
  * The set this subset belongs to.
  */
 final RelSet set;
  
 /**
  * best known plan
*/
 RelNode best;
  
 protected RelTraitSet traitSet;
 
 public Iterable<RelNode> getRels() {
   return () -> Linq4j.asEnumerable(set.rels)
       .where(v1 -> v1.getTraitSet().satisfies(traitSet))
       .iterator();
 }
    • 一个 RelSet 有一组 RelSubset, 而一个 RelSubset 引用一个 RelSet.
    • 一个 RelSet 可能有多种 trait (因为一组 RelNode 逻辑上等价, 物理上(trait) 不等价), 比如 在 [X] , [Y, Z] 上都进行了排序, 那么对于这个 RelSet 有两个 RelSubset
    • 通过 getRels 符合自己 trait 的 rels.

  • VolcanoRuleMatch/RuleCall :描述一次成功规则的匹配，包含 Rule 和被匹配的节点

  • RuleQueue:是一个优先队列，包含当前所有可行的 RuleMatch

  • Importance :描述 RuleMatch 的重要程度，importance 大的优先处理, 每一轮迭代都会实时调整.

      • 尽量对代价大的节点先做优化，从而尽可能在有限的优化次数内获得更大的收益, ost 越大、importance 也越大

  • Program: 用来组装优化的流程, 类似 pipeline 的感觉.

调用约定概念梳理

Calite 的特有概念, 为了支持多数据源(异构数据源)

Convention

  • 一种 RelTrait, 表一个物理实现

  • 几种实现

    1. Convention#Impl: 默认的 Convention, 只是提供了接口信息和名称信息 e.g.
    • Convention NONE = new Impl("NONE", RelNode.class);
    • SparkRel.CONVENTION: new Convention.Impl("SPARK", SparkRel.class);

    1. EnumerableConvention: 能返回 linq4j.Enumerable 的 Convention 实现, 默认一般用这个, 会使用 codegen.

    2. InterpretableConvention: 也是返回 Enumerable, 同样会实现 EnumerableRel 接口, 不通过 codegen 执行

    3. JdbcConvention,

    4. BindableConvention

ConverterRule:
  • 是 RelOptRule 的子类, 专门用来做数据源之间的转换
  • ConverterRule 一般会调用对应的 Converter 来完成工作, 比如说 JdbcToSparkConverterRule 调用 JdbcToSparkConverter 来完成对 JDBC Table 到 Spark RDD 的转换
  • Abstract base class for a rule which converts from one calling convention to another without changing semantics.
/** Planner rule that converts from Spark to enumerable convention. */
static class SparkToEnumerableConverterRule extends ConverterRule {
  public static final SparkToEnumerableConverterRule INSTANCE =
      new SparkToEnumerableConverterRule();

  private SparkToEnumerableConverterRule() {
    super(
        RelNode.class, SparkRel.CONVENTION, EnumerableConvention.INSTANCE,
        "SparkToEnumerableConverterRule");
  }

  @Override public RelNode convert(RelNode rel) {
    return new SparkToEnumerableConverter(rel.getCluster(),
        rel.getTraitSet().replace(EnumerableConvention.INSTANCE), rel);
  }
}
Converter:
  • 一种特殊的 RelNode (这是一个 RelNode)
  • 后续用 ExpandConversionRule 来调用 TraitDef 的 convert 来做转换, 关于 convention, 需要 isGuaranteed 为 true, 但是在 calcite 代码里并没有这个转换, 其他使用 Calcite 的引擎有, 如 Drill.
  • 由 Converter Rule 转化而成
  • 举个例子: SparkToEnumerableConverterRule 实现了 SparkRel.CONVENTION 到 EnumerableConvention 的转换，生成了SparkToEnumerableConverter , 对应没有这一套是会生成一个AbstractConverter 在根节点, 所以他们的子节点的 trait (Convention) 会以他们为准(ENUMERABLE/SPARK).
  • 再次强调这个 SparkToEnumerableConverter 是一个 RelNode, 可以看看这个节点怎么生成物理执行计划:
public Result implement(EnumerableRelImplementor implementor, Prefer pref) {
  // Generate:
  //   RDD rdd = ...;
  //   return SparkRuntime.asEnumerable(rdd);
  final BlockBuilder list = new BlockBuilder();
  final SparkRel child = (SparkRel) getInput();
  final PhysType physType =
      PhysTypeImpl.of(implementor.getTypeFactory(),
          getRowType(),
          JavaRowFormat.CUSTOM);
  SparkRel.Implementor sparkImplementor =
      new SparkImplementorImpl(implementor);
  final SparkRel.Result result = child.implementSpark(sparkImplementor);
  final Expression rdd = list.append("rdd", result.block);
  final Expression enumerable =
      list.append(
          "enumerable",
          Expressions.call(
              SparkMethod.AS_ENUMERABLE.method,
              rdd));
  list.add(Expressions.return_(null, enumerable));
  return implementor.result(physType, list.toBlock());
}

SparkMethod.AS_ENUMERABLE.method:

AS_ENUMERABLE(SparkRuntime.class, "asEnumerable", JavaRDD.class)

在 SparkRuntime 类中可以找到 asEnumerable 方法:

/** Converts an RDD into an enumerable. */
public static <T> Enumerable<T> asEnumerable(JavaRDD<T> rdd) {
  return Linq4j.asEnumerable(rdd.collect());
}

image-20200208225453064

基本流程

入口点

测试 SQL: select * from emps where name = ‘John’

1. Prepare

看代码中最后的 return 语句, 加进来了一系列的优化:

  1. Hep Planner 做一些子查询的优化 (SubQueryRemoveRule.FILTER, SubQueryRemoveRule.PROJECT, SubQueryRemoveRule.JOIN)
  2. DecorrelateProgram/TrimFieldsProgram
  3. Volcano Planner, 默认会注册一些 rule, 在 RelOptUtil#registerDefaultRules
  4. Hep Planner# RelOptRules.CALC_RULES

对 VolcanoPlanner 的使用就是在第3步, 我们主要关注这一步

protected RelRoot optimize(RelRoot root) {
      final Program program = getProgram();
      
      final RelNode rootRel4 = program.run(planner, root.rel, desiredTraits, materializationList, latticeList);
  }

  public static Program standard(RelMetadataProvider metadataProvider) {
  final Program program1 =
      (planner, rel, requiredOutputTraits, materializations, lattices) -> {
        planner.setRoot(rel);

        for (RelOptMaterialization materialization : materializations) {
          planner.addMaterialization(materialization);
        }
        for (RelOptLattice lattice : lattices) {
          planner.addLattice(lattice);
        }

        final RelNode rootRel2 =
            rel.getTraitSet().equals(requiredOutputTraits)
                ? rel
                : planner.changeTraits(rel, requiredOutputTraits);
        assert rootRel2 != null;

        planner.setRoot(rootRel2);
        final RelOptPlanner planner2 = planner.chooseDelegate();
        final RelNode rootRel3 = planner2.findBestExp();
        assert rootRel3 != null : "could not implement exp";
        return rootRel3;
      };

  return sequence(subQuery(metadataProvider),
      new DecorrelateProgram(),
      new TrimFieldsProgram(),
      program1,

      // Second planner pass to do physical "tweaks". This the first time
      // that EnumerableCalcRel is introduced.
      calc(metadataProvider));
}

set default rule/trait

CalcitePrepareImpl#createPlanner

final VolcanoPlanner planner = new VolcanoPlanner(costFactory, externalContext);

planner.addRelTraitDef(ConventionTraitDef.INSTANCE);
if (CalciteSystemProperty.ENABLE_COLLATION_TRAIT.value()) {
  planner.addRelTraitDef(RelCollationTraitDef.INSTANCE);
}

RelOptUtil.registerDefaultRules(planner,prepareContext.config().materializationsEnabled(), enableBindable);

VolcanoPlanner#addRule

  • RelOptRuleOperand
    • e.g. Join(Filter, Any)

在优化时，哪个 RelNode 可以应用哪些 Rule 都已经提前记录好了, 是一个 MultiMap，一个RelNode可以对应于多个 operands.

后面还会调用 rule 的 onMatch 进行筛选, 这里只是很粗粒度的

// Each of this rule's operands is an 'entry point' for a rule call.
// Register each operand against all concrete sub-classes that could match it.
for (RelOptRuleOperand operand : rule.getOperands()) {
  for (Class<? extends RelNode> subClass: subClasses(operand.getMatchedClass())) {
    classOperands.put(subClass, operand);
  }
}

match 的具体位置在:

org.apache.calcite.plan.volcano.VolcanoRuleCall#matchRecurse

...
if (getRule().matches(this)) {
  onMatch();
}
...

setRoot (1st)

初始进入 optimizer 时的执行计划的 dump plan:

LogicalProject(EMPNO=[$0], NAME=[$1], DEPTNO=[$2], GENDER=[$3], CITY=[$4], EMPID=[$5], AGE=[$6], SLACKER=[$7], MANAGER=[$8], JOINEDAT=[$9])
  LogicalFilter(condition=[=($1, 'John')])
    LogicalTableScan(table=[[SALES, EMPS]])

核心方法是 registerImpl

public void setRoot(RelNode rel) {
  this.root = registerImpl(rel, null);
  
  if (this.originalRoot == null) {
    this.originalRoot = rel;
  }

  // Making a node the root changes its importance.
  this.ruleQueue.recompute(this.root);
  ensureRootConverters();
}

private RelSubset registerImpl(
      RelNode rel,
      RelSet set) {
    ...
  if (rel instanceof RelSubset) {
    return registerSubset(set, (RelSubset) rel);
  }
  
  // 递归调用
  rel = rel.onRegister(this);
  
  // Place the expression in the appropriate equivalence set.
  if (set == null) {
    set = new RelSet(
        nextSetId++,
        Util.minus(
            RelOptUtil.getVariablesSet(rel),
            rel.getVariablesSet()),
        RelOptUtil.getVariablesUsed(rel));
    this.allSets.add(set);
  }
  
  // **把 RelNode 加到 RelSet 中, 并且返回一个 RelSubset, 因为新加入的 rel 可能有之前没有过的 trait, 所以可能生成一个新的 RelSubset**
  RelSubset subset = addRelToSet(rel, set);
  
  ...
  // Queue up all rules triggered by this relexp's creation.
  fireRules(rel, true);

  // It's a new subset.
  if (set.subsets.size() > subsetBeforeCount) {
    fireRules(subset, true);
  }

  return subset;
  
}

private RelSubset addRelToSet(RelNode rel, RelSet set) {
  RelSubset subset = set.add(rel);
  mapRel2Subset.put(rel, subset);

  // While a tree of RelNodes is being registered, sometimes nodes' costs
  // improve and the subset doesn't hear about it. You can end up with
  // a subset with a single rel of cost 99 which thinks its best cost is
  // 100. We think this happens because the back-links to parents are
  // not established. So, give the subset another chance to figure out
  // its cost.
  final RelMetadataQuery mq = rel.getCluster().getMetadataQuery();
  try {
    subset.propagateCostImprovements(this, mq, rel, new HashSet<>());
  } catch (CyclicMetadataException e) {
    // ignore
  }

  return subset;
}

onRegister

注意, 这里更新了 RelNode 的 input 为 RelSubSet(ensureRegistered).

public RelNode onRegister(RelOptPlanner planner) {
  List<RelNode> oldInputs = getInputs();
  List<RelNode> inputs = new ArrayList<>(oldInputs.size());
  for (final RelNode input : oldInputs) {
    RelNode e = planner.ensureRegistered(input, null);
        
    // 更新 input 为 RelSubset
    inputs.add(e);
  }
  RelNode r = this;
  if (!Util.equalShallow(oldInputs, inputs)) {
    r = copy(getTraitSet(), inputs);
  }
  r.recomputeDigest();
  assert r.isValid(Litmus.THROW, null);
  return r;
}

ensureRegistered

输入是两个等价的 RelNode, 返回一个 RelSubset

public RelSubset ensureRegistered(RelNode rel, RelNode equivRel) {
  RelSubset result;
  final RelSubset subset = getSubset(rel);
  if (subset != null) {
    if (equivRel != null) {
      final RelSubset equivSubset = getSubset(equivRel);
      if (subset.set != equivSubset.set) {
        merge(equivSubset.set, subset.set);
      }
    }
    result = subset;
  } else {
    result = register(rel, equivRel);
  }

  // Checking if tree is valid considerably slows down planning
  // Only doing it if logger level is debug or finer
  if (LOGGER.isDebugEnabled()) {
    assert isValid(Litmus.THROW);
  }

  return result;
}

fireRules

上面的 fireRules deferred 为是 true, 意思就是不立即执行 rule, 可以看到DeferringRuleCall#onMatch 把 VolcanoRuleMatch 扔到了 rule queue 中, 等之后执行.

void fireRules(
    RelNode rel,
    boolean deferred) {
  for (RelOptRuleOperand operand : classOperands.get(rel.getClass())) {
    if (operand.matches(rel)) {
      final VolcanoRuleCall ruleCall;
      if (deferred) {
        ruleCall = new DeferringRuleCall(this, operand);
      } else {
        ruleCall = new VolcanoRuleCall(this, operand);
      }
      ruleCall.match(rel);
    }
  }
}

private static class DeferringRuleCall extends VolcanoRuleCall {
  DeferringRuleCall(
      VolcanoPlanner planner,
      RelOptRuleOperand operand) {
    super(planner, operand);
  }

  /**
   * Rather than invoking the rule (as the base method does), creates a
   * {@link VolcanoRuleMatch} which can be invoked later.
   */
  protected void onMatch() {
    final VolcanoRuleMatch match =
        new VolcanoRuleMatch(
            volcanoPlanner,
            getOperand0(),
            rels,
            nodeInputs);
    volcanoPlanner.ruleQueue.addMatch(match);
  }
}

总结:

输入是一颗 RelNode 的树， setRoot 是深度遍历, 将每个 RelNode 创建一个 RelSet 和一个包含初始 RelNode 的 RelSubSet, 并且把节点所能执行的 Rule 作为一个 VolcanoRuleCall 放入 RuleQueue. 最终生成的是一颗 RelSet 组成的树(子树在 RelSet.rels (RelNode#getInput), input 是 RelSubSet, RelSubSet 又记录了 RelSet, 以此类推, 构成一棵树)

image-20200201142854598

setRoot (2st)

首先会进行 RelNode rootRel2 = planner.changeTraits(rel, requiredOutputTraits)

public RelNode changeTraits(final RelNode rel, RelTraitSet toTraits) {
    RelSubset rel2 = ensureRegistered(rel, null);
  if (rel2.getTraitSet().equals(toTraits)) {
    return rel2;
  }
  return rel2.set.getOrCreateSubset(rel.getCluster(), toTraits.simplify());
}

在 changeTraits 里会创建一个新的 RelSubset(rel#16:Subset#2.ENUMERABLE.[]) 作为根节点(rootRel2), 但是两者还是同一个 RelSet (因为逻辑语义上没有变化)

image-20200130143902228

然后会再用这个 rootRel2 再 set 一次 root: planner.setRoot(rootRel2); 由于这次的根节点是 RelSubset, registerImpl 会走到 registerSubset 中去. 不过会直接返回出来.

接着会执行 ensureRootConverters , 这里发现当root 的traitSet 和 root 的 subset 的 traitSet 不想等的时候, 会添加一个 AbstractConverter 节点. 注意这个 AbstractConverter 是一个 RelNode.

void ensureRootConverters() {
  final Set<RelSubset> subsets = new HashSet<>();
  for (RelSubset subset : root.set.subsets) {
    final ImmutableList<RelTrait> difference = root.getTraitSet().difference(subset.getTraitSet());
    if (difference.size() == 1 && subsets.add(subset)) {
      register(new AbstractConverter(subset.getCluster(), subset,
              difference.get(0).getTraitDef(), root.getTraitSet()), root);
    }
  }
}

在这里会再一次的调用到 registerImpl, 不过这次 RelSet 已经不为空了.

在 registerImpl 中, 当发现节点是 Converter 时, 会尝试把 Converter merge 到其 child 所在是 RelSet 中(Converters are in the same set as their children.).

private RelSubset registerImpl(RelNode rel, RelSet set) {
  // Converters are in the same set as their children.
  if (rel instanceof Converter) {
    final RelNode input = ((Converter) rel).getInput();
    final RelSet childSet = getSet(input);
    if ((set != null) && (set != childSet) && (set.equivalentSet == null)) {
      LOGGER.trace("Register #{} {} (and merge sets, because it is a conversion)", rel.getId(), rel.getDigest());
      merge(set, childSet);
      registerCount++;
    } else {
      set = childSet;
    }
  }
}

这是经过第二次 set root 之后的树的关系

image-20200201143344730

整个过程 的 log:

TRACE - new RelSubset#9
TRACE - Register rel#0:LogicalTableScan.NONE.[](table=[SALES, EMPS]) in rel#9:Subset#0.NONE.[]
TRACE - Importance of [rel#9:Subset#0.NONE.[]] is 0.0
TRACE - OPTIMIZE Rule-match queued: rule [EnumerableTableScanRule] rels [rel#0:LogicalTableScan.NONE.[](table=[SALES, EMPS])]
TRACE - OPTIMIZE Rule-match queued: rule [BindableTableScanRule] rels [rel#0:LogicalTableScan.NONE.[](table=[SALES, EMPS])]
TRACE - OPTIMIZE Rule-match queued: rule [TableScanRule] rels [rel#0:LogicalTableScan.NONE.[](table=[SALES, EMPS])]
TRACE - new LogicalFilter#10
TRACE - new RelSubset#11
TRACE - Register rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John')) in rel#11:Subset#1.NONE.[]
TRACE - Importance of [rel#9:Subset#0.NONE.[]] to its parent [rel#11:Subset#1.NONE.[]] is 0.0 (parent importance=0.0, child cost=1.0E30, parent cost=1.0E30)
TRACE - Importance of [rel#9:Subset#0.NONE.[]] is 0.0
TRACE - Importance of [rel#11:Subset#1.NONE.[]] is 0.0
TRACE - OPTIMIZE Rule-match queued: rule [EnumerableFilterRule] rels [rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John'))]
TRACE - OPTIMIZE Rule-match queued: rule [FilterTableScanRule] rels [rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John')), rel#0:LogicalTableScan.NONE.[](table=[SALES, EMPS])]
TRACE - OPTIMIZE Rule-match queued: rule [MaterializedViewJoinRule(Filter)] rels [rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John'))]
TRACE - OPTIMIZE Rule-match queued: rule [MaterializedViewFilterScanRule] rels [rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John')), rel#0:LogicalTableScan.NONE.[](table=[SALES, EMPS])]
TRACE - new LogicalProject#12
TRACE - new RelSubset#13
TRACE - Register rel#12:LogicalProject.NONE.[](input=RelSubset#11,EMPNO=$0,NAME=$1,DEPTNO=$2,GENDER=$3,CITY=$4,EMPID=$5,AGE=$6,SLACKER=$7,MANAGER=$8,JOINEDAT=$9) in rel#13:Subset#2.NONE.[]
TRACE - Importance of [rel#11:Subset#1.NONE.[]] to its parent [rel#13:Subset#2.NONE.[]] is 0.0 (parent importance=0.0, child cost=1.0E30, parent cost=1.0E30)
TRACE - Importance of [rel#11:Subset#1.NONE.[]] is 0.0
TRACE - Importance of [rel#13:Subset#2.NONE.[]] is 0.0
TRACE - OPTIMIZE Rule-match queued: rule [EnumerableProjectRule] rels [rel#12:LogicalProject.NONE.[](input=RelSubset#11,EMPNO=$0,NAME=$1,DEPTNO=$2,GENDER=$3,CITY=$4,EMPID=$5,AGE=$6,SLACKER=$7,MANAGER=$8,JOINEDAT=$9)]
TRACE - OPTIMIZE Rule-match queued: rule [ProjectRemoveRule] rels [rel#12:LogicalProject.NONE.[](input=RelSubset#11,EMPNO=$0,NAME=$1,DEPTNO=$2,GENDER=$3,CITY=$4,EMPID=$5,AGE=$6,SLACKER=$7,MANAGER=$8,JOINEDAT=$9)]
TRACE - OPTIMIZE Rule-match queued: rule [MaterializedViewJoinRule(Project-Filter)] rels [rel#12:LogicalProject.NONE.[](input=RelSubset#11,EMPNO=$0,NAME=$1,DEPTNO=$2,GENDER=$3,CITY=$4,EMPID=$5,AGE=$6,SLACKER=$7,MANAGER=$8,JOINEDAT=$9), rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John'))]
TRACE - OPTIMIZE Rule-match queued: rule [ProjectFilterTransposeRule] rels [rel#12:LogicalProject.NONE.[](input=RelSubset#11,EMPNO=$0,NAME=$1,DEPTNO=$2,GENDER=$3,CITY=$4,EMPID=$5,AGE=$6,SLACKER=$7,MANAGER=$8,JOINEDAT=$9), rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John'))]
TRACE - Importance of [rel#13:Subset#2.NONE.[]] is 1.0
TRACE - new LogicalFilter#14
TRACE - Register: rel#14 is equivalent to rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John'))
TRACE - new LogicalProject#15
TRACE - Register: rel#15 is equivalent to rel#12:LogicalProject.NONE.[](input=RelSubset#11,EMPNO=$0,NAME=$1,DEPTNO=$2,GENDER=$3,CITY=$4,EMPID=$5,AGE=$6,SLACKER=$7,MANAGER=$8,JOINEDAT=$9)
TRACE - new RelSubset#16
TRACE - new AbstractConverter#17
TRACE - Register rel#17:AbstractConverter.ENUMERABLE.[](input=RelSubset#13,convention=ENUMERABLE,sort=[]) in rel#16:Subset#2.ENUMERABLE.[]
TRACE - Importance of [rel#13:Subset#2.NONE.[]] to its parent [rel#16:Subset#2.ENUMERABLE.[]] is 0.495 (parent importance=0.5, child cost=1.0E30, parent cost=1.0E30)
TRACE - Importance of [rel#13:Subset#2.NONE.[]] is 0.495
TRACE - Importance of [rel#16:Subset#2.ENUMERABLE.[]] is 1.0
TRACE - OPTIMIZE Rule-match queued: rule [ExpandConversionRule] rels [rel#17:AbstractConverter.ENUMERABLE.[](input=RelSubset#13,convention=ENUMERABLE,sort=[])]

findBestExp

这里有 4 个 phase, 但是真的有用的只有一个 OPTIMIZE 阶段.

setInitialImportance()

从 root 开始, 将 root SubSet 设置 importance 为 1.0, 之后的其他 children SubSet 设置 importance 为 pow(0.9, n), n 为 children 在的层数. 经过这一步之后的信息, 可以看到 importance 分别为 1, 0.9, 0.81…:

Root: rel#16:Subset#2.ENUMERABLE.[]
Original rel:
LogicalProject(EMPNO=[$0], NAME=[$1], DEPTNO=[$2], GENDER=[$3], CITY=[$4], EMPID=[$5], AGE=[$6], SLACKER=[$7], MANAGER=[$8], JOINEDAT=[$9]): rowcount = 15.0, cumulative cost = {130.0 rows, 351.0 cpu, 0.0 io}, id = 7
  LogicalFilter(condition=[=($1, 'John')]): rowcount = 15.0, cumulative cost = {115.0 rows, 201.0 cpu, 0.0 io}, id = 5
    LogicalTableScan(table=[[SALES, EMPS]]): rowcount = 100.0, cumulative cost = {100.0 rows, 101.0 cpu, 0.0 io}, id = 0

Sets:
Set#0, type: RecordType(INTEGER EMPNO, VARCHAR NAME, INTEGER DEPTNO, VARCHAR GENDER, VARCHAR CITY, INTEGER EMPID, INTEGER AGE, BOOLEAN SLACKER, BOOLEAN MANAGER, DATE JOINEDAT)
    rel#9:Subset#0.NONE.[], best=null, importance=0.7290000000000001
        rel#0:LogicalTableScan.NONE.[](table=[SALES, EMPS]), rowcount=100.0, cumulative cost={inf}
Set#1, type: RecordType(INTEGER EMPNO, VARCHAR NAME, INTEGER DEPTNO, VARCHAR GENDER, VARCHAR CITY, INTEGER EMPID, INTEGER AGE, BOOLEAN SLACKER, BOOLEAN MANAGER, DATE JOINEDAT)
    rel#11:Subset#1.NONE.[], best=null, importance=0.81
        rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John')), rowcount=15.0, cumulative cost={inf}
Set#2, type: RecordType(INTEGER EMPNO, VARCHAR NAME, INTEGER DEPTNO, VARCHAR GENDER, VARCHAR CITY, INTEGER EMPID, INTEGER AGE, BOOLEAN SLACKER, BOOLEAN MANAGER, DATE JOINEDAT)
    rel#13:Subset#2.NONE.[], best=null, importance=0.9
        rel#12:LogicalProject.NONE.[](input=RelSubset#11,EMPNO=$0,NAME=$1,DEPTNO=$2,GENDER=$3,CITY=$4,EMPID=$5,AGE=$6,SLACKER=$7,MANAGER=$8,JOINEDAT=$9), rowcount=15.0, cumulative cost={inf}
    rel#16:Subset#2.ENUMERABLE.[], best=null, importance=1.0
        rel#17:AbstractConverter.ENUMERABLE.[](input=RelSubset#13,convention=ENUMERABLE,sort=[]), rowcount=15.0, cumulative cost={inf}

由于现在还没有一个完整的物理执行计划, 所以整个计划的 best cost 还是 inf.

RuleQueue#popMatch

VolcanoRuleMatch match = ruleQueue.popMatch(phase);
match.onMatch();

在初始 RuleQueue 里有 13 个 Rule Match.

rule [ExpandConversionRule] rels [rel#17:AbstractConverter.ENUMERABLE.[](input=RelSubset#13,convention=ENUMERABLE,sort=[])]
rule [EnumerableProjectRule(in:NONE,out:ENUMERABLE)] rels [rel#12:LogicalProject.NONE.[](input=RelSubset#11,EMPNO=$0,NAME=$1,DEPTNO=$2,GENDER=$3,CITY=$4,EMPID=$5,AGE=$6,SLACKER=$7,MANAGER=$8,JOINEDAT=$9)]"
rule [ProjectRemoveRule] rels [rel#12:LogicalProject.NONE.[](input=RelSubset#11,EMPNO=$0,NAME=$1,DEPTNO=$2,GENDER=$3,CITY=$4,EMPID=$5,AGE=$6,SLACKER=$7,MANAGER=$8,JOINEDAT=$9)]"
rule [ProjectFilterTransposeRule] rels [rel#12:LogicalProject.NONE.[](input=RelSubset#11,EMPNO=$0,NAME=$1,DEPTNO=$2,GENDER=$3,CITY=$4,EMPID=$5,AGE=$6,SLACKER=$7,MANAGER=$8,JOINEDAT=$9), rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John'))]"
rule [MaterializedViewJoinRule(Project-Filter)] rels [rel#12:LogicalProject.NONE.[](input=RelSubset#11,EMPNO=$0,NAME=$1,DEPTNO=$2,GENDER=$3,CITY=$4,EMPID=$5,AGE=$6,SLACKER=$7,MANAGER=$8,JOINEDAT=$9), rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John'))]"
rule [ReduceExpressionsRule(Filter)] rels [rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John'))]"
rule [MaterializedViewFilterScanRule] rels [rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John')), rel#0:LogicalTableScan.NONE.[](table=[SALES, EMPS])]"
rule [FilterTableScanRule] rels [rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John')), rel#0:LogicalTableScan.NONE.[](table=[SALES, EMPS])]"
rule [MaterializedViewJoinRule(Filter)] rels [rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John'))]"
rule [EnumerableFilterRule(in:NONE,out:ENUMERABLE)] rels [rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John'))]"
rule [TableScanRule] rels [rel#0:LogicalTableScan.NONE.[](table=[SALES, EMPS])]"
rule [BindableTableScanRule] rels [rel#0:LogicalTableScan.NONE.[](table=[SALES, EMPS])]"
rule [EnumerableTableScanRule(in:NONE,out:ENUMERABLE)] rels [rel#0:LogicalTableScan.NONE.[](table=[SALES, EMPS])]"

挑几个 rule 来举例子:

EnumerableProjectRule

可以看到在 convert 方法中生成了一个 EnumerableProject, 然后 transformTo 了过去.

/**
 * Rule to convert a {@link org.apache.calcite.rel.logical.LogicalProject} to an
 * {@link EnumerableProject}.
 */
class EnumerableProjectRule extends ConverterRule {
  EnumerableProjectRule() {
    super(LogicalProject.class,
        (Predicate<LogicalProject>) RelOptUtil::containsMultisetOrWindowedAgg,
        Convention.NONE, EnumerableConvention.INSTANCE,
        RelFactories.LOGICAL_BUILDER, "EnumerableProjectRule");
  }

    public void onMatch(RelOptRuleCall call) {
    RelNode rel = call.rel(0);
    if (rel.getTraitSet().contains(inTrait)) {
      final RelNode converted = convert(rel);
      if (converted != null) {
        call.transformTo(converted);
      }
    }
  }
  
  public RelNode convert(RelNode rel) {
    final LogicalProject project = (LogicalProject) rel;
    return EnumerableProject.create(
        convert(project.getInput(),
            project.getInput().getTraitSet()
                .replace(EnumerableConvention.INSTANCE)),
        project.getProjects(),
        project.getRowType());
  }

  /**
   * Converts a relation expression to a given set of traits, if it does not
   * already have those traits.
   *
   * @param rel      Relational expression to convert
   * @param toTraits desired traits
   * @return a relational expression with the desired traits; never null
   */
  public static RelNode convert(RelNode rel, RelTraitSet toTraits) {
    RelOptPlanner planner = rel.getCluster().getPlanner();

    RelTraitSet outTraits = rel.getTraitSet();
    for (int i = 0; i < toTraits.size(); i++) {
      RelTrait toTrait = toTraits.getTrait(i);
      if (toTrait != null) {
        outTraits = outTraits.replace(i, toTrait);
      }
    }

    if (rel.getTraitSet().matches(outTraits)) {
      return rel;
    }
    return planner.changeTraits(rel, outTraits);
  }
}

在方法 transformTo 中可以看到 register 了这个新生成的物理算子(EnumerableProject): volcanoPlanner.ensureRegistered(rel, rels[0], this), 这次第二个参数 equivRel 不是 null 了, 而是 rels[0](LogicalProject). 在 VolcanoPlanner#register 中会拿到 equivRel 对应的 RelSet, 再走下去就又是上面的 registerImpl, 只不过这时候会有一个 set 传入.

image-20200130235141251

这样就把新生成的这个物理算子注册到了原来的 RelSet 树上, 完成了 transform 的过程.

由于有新的算子生成(EnumerableProject), fireRule 会匹配一条新的规则到 ruleQueue 中.

OPTIMIZE Rule-match queued: rule [ProjectRemoveRule] rels [rel#19:EnumerableProject.ENUMERABLE.[](input= ...]

Before

image-20200208225742683

After

image-20200208225746328

ProjectRemoveRule

因为查询是 select * , 所以可以直接取出这个 projection.

public void onMatch(RelOptRuleCall call) {
  Project project = call.rel(0);
  RelNode stripped = project.getInput();
  ...
  RelNode child = call.getPlanner().register(stripped, project);
  call.transformTo(child);
}

可以看到这个规则直接就把 project 节点的 child 注册到当前 RelSet, 也就是说直接消除了这个 projection, 故这个新的 plan 的 cost 一定会比原来低.

merge: RelSet merge(RelSet set, RelSet set2): 合并两个 RelSet.

RelSet#mergeWith(VolcanoPlanner planner, RelSet otherSet)

for (RelSubset otherSubset : otherSet.subsets) {
  planner.ruleQueue.subsetImportances.remove(otherSubset);
  RelSubset subset = getOrCreateSubset(
          otherSubset.getCluster(),
          otherSubset.getTraitSet());
  // collect RelSubset instances, whose best should be changed
  if (otherSubset.bestCost.isLt(subset.bestCost)) {
    changedSubsets.put(subset, otherSubset.best);
  }
  for (RelNode otherRel : otherSubset.getRels()) {
    planner.reregister(this, otherRel);
  }
}

经过这个 rule 之后, 来看看 root 的 relset 的前后对比, 可以看到在这一层多了一个 logical filter.

before

image-20200131214015824

after

image-20200131214114884

Root: rel#18:Subset#1.ENUMERABLE.[]
Original rel:
LogicalProject(EMPNO=[$0], NAME=[$1], DEPTNO=[$2], GENDER=[$3], CITY=[$4], EMPID=[$5], AGE=[$6], SLACKER=[$7], MANAGER=[$8], JOINEDAT=[$9]): rowcount = 15.0, cumulative cost = {130.0 rows, 351.0 cpu, 0.0 io}, id = 7
  LogicalFilter(condition=[=($1, 'John')]): rowcount = 15.0, cumulative cost = {115.0 rows, 201.0 cpu, 0.0 io}, id = 5
    LogicalTableScan(table=[[SALES, EMPS]]): rowcount = 100.0, cumulative cost = {100.0 rows, 101.0 cpu, 0.0 io}, id = 0

Sets:
Set#0, type: RecordType(INTEGER EMPNO, VARCHAR NAME, INTEGER DEPTNO, VARCHAR GENDER, VARCHAR CITY, INTEGER EMPID, INTEGER AGE, BOOLEAN SLACKER, BOOLEAN MANAGER, DATE JOINEDAT)
  rel#9:Subset#0.NONE.[], best=null, importance=0.7290000000000001
    rel#0:LogicalTableScan.NONE.[](table=[SALES, EMPS]), rowcount=100.0, cumulative cost={inf}

Set#1, type: RecordType(INTEGER EMPNO, VARCHAR NAME, INTEGER DEPTNO, VARCHAR GENDER, VARCHAR CITY, INTEGER EMPID, INTEGER AGE, BOOLEAN SLACKER, BOOLEAN MANAGER, DATE JOINEDAT)
  rel#11:Subset#1.NONE.[], best=null, importance=0.81
    rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John')), rowcount=15.0, cumulative cost={inf}
    rel#12:LogicalProject.NONE.[](input=RelSubset#11,EMPNO=$0,NAME=$1,DEPTNO=$2,GENDER=$3,CITY=$4,EMPID=$5,AGE=$6,SLACKER=$7,MANAGER=$8,JOINEDAT=$9), rowcount=15.0, cumulative cost={inf}
  rel#18:Subset#1.ENUMERABLE.[], best=null, importance=0.405
    rel#17:AbstractConverter.ENUMERABLE.[](input=RelSubset#11,convention=ENUMERABLE,sort=[]), rowcount=15.0, cumulative cost={inf}
    rel#19:EnumerableProject.ENUMERABLE.[](input=RelSubset#18,EMPNO=$0,NAME=$1,DEPTNO=$2,GENDER=$3,CITY=$4,EMPID=$5,AGE=$6,SLACKER=$7,MANAGER=$8,JOINEDAT=$9), rowcount=15.0, cumulative cost={inf}

经过所有规则之后:

Root: rel#18:Subset#1.ENUMERABLE.[]
Original rel:
LogicalProject(EMPNO=[$0], NAME=[$1], DEPTNO=[$2], GENDER=[$3], CITY=[$4], EMPID=[$5], AGE=[$6], SLACKER=[$7], MANAGER=[$8], JOINEDAT=[$9]): rowcount = 15.0, cumulative cost = {130.0 rows, 351.0 cpu, 0.0 io}, id = 7
  LogicalFilter(condition=[=($1, 'John')]): rowcount = 15.0, cumulative cost = {115.0 rows, 201.0 cpu, 0.0 io}, id = 5
    LogicalTableScan(table=[[SALES, EMPS]]): rowcount = 100.0, cumulative cost = {100.0 rows, 101.0 cpu, 0.0 io}, id = 0

Sets:
Set#0, type: RecordType(INTEGER EMPNO, VARCHAR NAME, INTEGER DEPTNO, VARCHAR GENDER, VARCHAR CITY, INTEGER EMPID, INTEGER AGE, BOOLEAN SLACKER, BOOLEAN MANAGER, DATE JOINEDAT)
  rel#9:Subset#0.NONE.[], best=null, importance=0.81
    rel#0:LogicalTableScan.NONE.[](table=[SALES, EMPS]), rowcount=100.0, cumulative cost={inf}
  rel#22:Subset#0.ENUMERABLE.[], best=rel#27, importance=0.9
    rel#27:EnumerableInterpreter.ENUMERABLE.[](input=RelSubset#26), rowcount=100.0, cumulative cost={51.0 rows, 51.01 cpu, 0.0 io}
  rel#26:Subset#0.BINDABLE.[], best=rel#25, importance=0.81
    rel#25:BindableTableScan.BINDABLE.[](table=[SALES, EMPS]), rowcount=100.0, cumulative cost={1.0 rows, 1.01 cpu, 0.0 io}
Set#1, type: RecordType(INTEGER EMPNO, VARCHAR NAME, INTEGER DEPTNO, VARCHAR GENDER, VARCHAR CITY, INTEGER EMPID, INTEGER AGE, BOOLEAN SLACKER, BOOLEAN MANAGER, DATE JOINEDAT)
  rel#11:Subset#1.NONE.[], best=null, importance=0.9
    rel#10:LogicalFilter.NONE.[](input=RelSubset#9,condition==($1, 'John')), rowcount=15.0, cumulative cost={inf}
    rel#12:LogicalProject.NONE.[](input=RelSubset#11,EMPNO=$0,NAME=$1,DEPTNO=$2,GENDER=$3,CITY=$4,EMPID=$5,AGE=$6,SLACKER=$7,MANAGER=$8,JOINEDAT=$9), rowcount=15.0, cumulative cost={inf}
  rel#18:Subset#1.ENUMERABLE.[], best=rel#30, importance=1.0
    rel#17:AbstractConverter.ENUMERABLE.[](input=RelSubset#11,convention=ENUMERABLE,sort=[]), rowcount=15.0, cumulative cost={inf}
    rel#19:EnumerableProject.ENUMERABLE.[](input=RelSubset#18,EMPNO=$0,NAME=$1,DEPTNO=$2,GENDER=$3,CITY=$4,EMPID=$5,AGE=$6,SLACKER=$7,MANAGER=$8,JOINEDAT=$9), rowcount=100.0, cumulative cost={150.5 rows, 1050.505 cpu, 0.0 io}
    rel#23:EnumerableFilter.ENUMERABLE.[](input=RelSubset#22,condition==($1, 'John')), rowcount=15.0, cumulative cost={66.0 rows, 151.01 cpu, 0.0 io}
    rel#30:EnumerableInterpreter.ENUMERABLE.[](input=RelSubset#21), rowcount=100.0, cumulative cost={50.5 rows, 50.505 cpu, 0.0 io}
  rel#21:Subset#1.BINDABLE.[], best=rel#20, importance=0.9
    rel#20:BindableTableScan.BINDABLE.[](table=[SALES, EMPS],filters=[=($1, 'John')]), rowcount=100.0, cumulative cost={0.5 rows, 0.505 cpu, 0.0 io}

image-20200131224924660

buildCheapestPlan

从 root 开始一路选subset 中的 best, 就得到了一颗最优的树:

EnumerableInterpreter
  BindableTableScan(table=[[SALES, EMPS]], filters=[[=($1, 'John')]])

优化策略

  1. 第一次找到可执行计划的计划(cost 不为 inf), 其对应的 Cost 暂时记为 BestCost
  2. 制定下一次优化要达到的目标为 BestCost*0.9，再根据当前的迭代次数计算 giveUpTick，这个值代表的意思是：如果迭代次数超过这个值还没有达到优化目标，那么将会放弃迭代
  3. 如果 RuleQueue 中 RuleMatch 为空，那么也会退出迭代
  4. 在每次迭代时都会从 RuleQueue 中选择一个 RuleMatch，策略是选择一个最高 importance 的 RuleMatch
  5. 最后根据 best plan，构建其对应的 RelNode

总结

优点:

  1. 为异构数据源提供了原生支持 (conversion 机制)
  2. volcano 的优化规则顺序不需要人工保证, 因为每次新生成一个节点, 会去执行 fireRule, 找到所有这个节点感兴趣的 rule 放到 RuleQueue 中, 当同时带来缺点 1.

缺点:

  1. CBO 是个伪命题, 执行的框架模型很不错, 但是如何准确预估 cost 是难点(Filter/Join/Agg)
  2. 更好的方式是做成 runtime 的, 一边执行, 一边根据执行的 stats 调整查询计划的形式(ae/runtime filter).
  3. 重复应用规则, 有一些(很多)规则会同时被 logical/physical nodes 触发, 比如 ProjectRemoveRule 接受 Project.class, 而 Project 是逻辑/物理 Project 的 基类, 很多规则仅在逻辑执行计划上 apply 就可以了. 在社区的讨论中, 去掉了这种 case, planning 的时间提升 30% (CALCITE-2970);
  4. Calcite 的 Volcano Planner 没有任何剪枝, 举例来说, 当计算一个计划到某个节点的 cost 已经比之前的 best 都高了, 则可以剪去这一枝;
  5. 本质上还是一个单机的优化引擎, 没有考虑分布式的优化(对比 Spark , Calcite 可以理解为无物理优化)
    1. 默认优化流程里根本没有加入 RelDistribution 的考虑, 也没有提供配置
    2. Distribution 也不是分布式的, 举例 calcite 的 HASH_DISTRIBUTED 值考虑了 Key, hash 的 func/num 都没有记录
    3. Aggregate 只有一种, 对比 Spark 有多种策略(planAggregateWithoutDistinct/planAggregateWithOneDistinct/planStreamingAggregation)
    4. 有多种 join, 但是 join 策略比较弱, 没有考虑数据量(对比 Spark 的 JoinSelection), 也不支持 hint
  6. 引入 AbstractConverter 来做 Spark ensureRequirement 类似的事情, 但是做的方式比较别扭, AbstractConverter 是一个执行计划中的节点, 会触发一个特定的规则 ExpandConversionRule 来保证 Distribution/Sort/Convertion, 邮件列表中讨论到这种方式污染了规则的 search space(polluting the search space), 导致了3-9 倍不必要的规则触发(对比 spark 是递归处理 root-child, calcite 每次只处理父子两个节点, 不递归). 由于它潜在的性能问题(CALCITE-2970 ), AbstractConverter 在 Calcite 代码中是默认关闭的, 详见社区讨论: Volcano’s problem with trait propagation: current state and future;
  7. 复杂度高, 不利于调试问题.
SELECT u.id AS user_id,
    u.name AS user_name,
    j.company AS user_company,
    u.age AS user_age
FROM users u
JOIN jobs j ON u.id=j.id
WHERE u.age > 30
 AND j.id>10
ORDER BY user_id"

共形成了 31 个 group(relset), 1000 个节点(开启了3 种 RelTraitDef)

Ref

[1]. https://zhuanlan.zhihu.com/p/58801070

[2]. https://zhuanlan.zhihu.com/p/60223655