impala fe 接受 be 请求
public byte[] createExecRequest(byte[] thriftQueryContext)
    TExecRequest result = frontend_.createExecRequest(planCtx);
       TExecRequest result = getTExecRequest(planCtx, timeline);
            TExecRequest req = doCreateExecRequest(planCtx, timeline);
                TExecRequest result = createBaseExecRequest(queryCtx, analysisResult);
                TQueryExecRequest queryExecRequest = getPlannedExecRequest(planCtx, analysisResult, timeline);
                    TQueryExecRequest queryExecRequest = createExecRequest(planner, planCtx);
                    List<PlanFragment> planRoots = planner.createPlans();(这里便是查询计划的生成)
                result.setQuery_exec_request(queryExecRequest);
​
​
public List<PlanFragment> createPlans() throws ImpalaException {
createPlans
List<PlanFragment> distrPlan = createPlanFragments();
    createPlanFragments
    SingleNodePlanner singleNodePlanner = new SingleNodePlanner(ctx_);
    DistributedPlanner distributedPlanner = new DistributedPlanner(ctx_);
    PlanNode singleNodePlan = singleNodePlanner.createSingleNodePlan();
    createSingleNodePlan
        createQueryPlan
            createSelectPlan
            createCheapestJoinPlan
                createJoinPlan(获得最优的SingleNodePlan,CBO)
​
​
    根据singleNodePlan 生成分布式fragemnts 
    fragments = distributedPlanner.createPlanFragments(singleNodePlan);
    createPlanFragments(createPlanFragments 递归)
        createHashJoinFragment/createScanFragment(这里为关联的节点构建ExchangeNode)
​
​
    rootFragment.verifyTreeCtx(ctx_);
    (这里有一些比较关键的逻辑
    1. ExchangeNode.size == ChildrenNode.size
    2. collectPlanNodes 获取当前PlanNode 及Children的所有Node
​
​
ParallelPlanner planner = new ParallelPlanner(ctx_);
List<PlanFragment> parallelPlans = planner.createPlans(distrPlan.get(0));
    createPlans
    createBuildPlans(root, null);
        createBuildPlans
            1. collectJoins()
            2. list join join and createBuildPlan (create Join build by ExChangeNode)
            3. list children and createBuildPlan(children)(遍历子节点的)

computeParentAndSubplanRefs 统计所有表及表相相关的查询信息,比如join 
​
parentRefPlans 存储了所有表的信息，保存在remainingRefs，createJoinPlan 会遍历所有的表，根据 candidate 排序，选择最大的一个表作为根
  // consumption of the materialized hash tables required for the join sequence
  Collections.sort(candidates,
                   new Comparator<Pair<TableRef, Long>>() {
                     @Override
                     public int compare(Pair<TableRef, Long> a, Pair<TableRef, Long> b) {
                       long diff = b.second - a.second;
                       return (diff < 0 ? -1 : (diff > 0 ? 1 : 0));
                     }
                   });
​
遍历跟当前表所有相关的表，计算他们相关的计算比如HashJoin 的代价 Cardinality,选择最小的Cardinality 作为优先的Join 顺序.遍历完成当前表，则进入下一张表，从remainingRefs 剔除已经计算过的表.直至从remainingRefs 为空，死循环结束
  
while(true)
{
   getMinCardinary();
   remove(minEntry)
}

​
1. broadcast join 
2. shuffle join 
​
​
执行流程
1. 获取左子树 lhsTree getCardinality
2. 获取右子树 rhsTree getCardinality
​
3. 推测join 算法，构建Exchange Node,
4. 在 lhsTree, rhsTree 选择Cardinality大的问题左子树，即根节点
​
5. Partiton Hash Join 
(推测表是否为Partiton 分区，优化查询计划)

1. coll   public List<PlanNode> collectPlanNodes() {
    List<PlanNode> nodes = new ArrayList<>();
    collectPlanNodesHelper(planRoot_, Predicates.alwaysTrue(), nodes);
    return nodes;
  }

   */
  private void createBuildPlans(PlanFragment fragment, CohortId buildCohortId) {
    List<JoinNode> joins = new ArrayList<>();
    collectJoins(fragment.getPlanRoot(), joins);
    if (!joins.isEmpty()) {
      if (buildCohortId == null) buildCohortId = cohortIdGenerator_.getNextId();
      for (JoinNode join: joins) createBuildPlan(join, buildCohortId);
    }
​
    for (PlanFragment child: fragment.getChildren()) {
      // We already recursed on the join build fragment in createBuildPlan().
      if (child.getSink() instanceof JoinBuildSink) continue;
      // Propagate the plan and cohort IDs to children that are part of the same plan.
      child.setPlanId(fragment.getPlanId());
      child.setCohortId(fragment.getCohortId());
      createBuildPlans(child, buildCohortId);
    }
  }
​