dremio 的job 分析参考示例

dremio 官方专门提供了一个章节，讲解如何进行分析，一些是一个总结，以及学习

预备

下载profiles，以及上传到dremio 中（个人默认space中）因为profiles 默认是json 格式的，我们直接使用dremio 查询分析数据

需要关注的内容:

sql 查询：sql 查询是否符合预期
query 源： 数据是否在需要的数据源中
计划时间： 时间是否符合预期
非默认参数：查看是否有非模式参数使用了
计划数据： 查看是否使用了rows ，row count 会造成比较高昂的广播，构建，构建会将数据加载到内存中
加速数据：包含了反射的选择，系统活跃情况
我们需要重点关注的是线程状态，通过此了解系统的等待，阻塞情况
错误处理：dremio nodes 情况，oom
性能考虑： 计划时间以及执行实现，线程数，row count 以及row，阻塞以及睡眠，准备以及等待时间

operator_type 数据准备

• 准备操作类型映射
  operator_type.json 内容如下

 

{"NAME":"SINGLE_SENDER","ID":0}

{"NAME":"BROADCAST_SENDER","ID":1}

{"NAME":"FILTER","ID":2}

{"NAME":"HASH_AGGREGATE","ID":3}

{"NAME":"HASH_JOIN","ID":4}

{"NAME":"MERGE_JOIN","ID":5}

{"NAME":"HASH_PARTITION_SENDER","ID":6}

{"NAME":"LIMIT","ID":7}

{"NAME":"MERGING_RECEIVER","ID":8}

{"NAME":"ORDERED_PARTITION_SENDER","ID":9}

{"NAME":"PROJECT","ID":10}

{"NAME":"UNORDERED_RECEIVER","ID":11}

{"NAME":"RANGE_SENDER","ID":12}

{"NAME":"SCREEN","ID":13}

{"NAME":"SELECTION_VECTOR_REMOVER","ID":14}

{"NAME":"STREAMING_AGGREGATE","ID":15}

{"NAME":"TOP_N_SORT","ID":16}

{"NAME":"EXTERNAL_SORT","ID":17}

{"NAME":"TRACE","ID":18}

{"NAME":"UNION","ID":19}

{"NAME":"OLD_SORT","ID":20}

{"NAME":"PARQUET_ROW_GROUP_SCAN","ID":21}

{"NAME":"HIVE_SUB_SCAN","ID":22}

{"NAME":"SYSTEM_TABLE_SCAN","ID":23}

{"NAME":"MOCK_SUB_SCAN","ID":24}

{"NAME":"PARQUET_WRITER","ID":25}

{"NAME":"DIRECT_SUB_SCAN","ID":26}

{"NAME":"TEXT_WRITER","ID":27}

{"NAME":"TEXT_SUB_SCAN","ID":28}

{"NAME":"JSON_SUB_SCAN","ID":29}

{"NAME":"INFO_SCHEMA_SUB_SCAN","ID":30}

{"NAME":"COMPLEX_TO_JSON","ID":31}

{"NAME":"PRODUCER_CONSUMER","ID":32}

{"NAME":"HBASE_SUB_SCAN","ID":33}

{"NAME":"WINDOW","ID":34}

{"NAME":"NESTED_LOOP_JOIN","ID":35}

{"NAME":"AVRO_SUB_SCAN","ID":36}

{"NAME":"MONGO_SUB_SCAN","ID":37}

{"NAME":"ELASTICSEARCH_SUB_SCAN","ID":38}

{"NAME":"ELASTICSEARCH_AGGREGATOR_SUB_SCAN","ID":39}

{"NAME":"FLATTEN","ID":40}

{"NAME":"EXCEL_SUB_SCAN","ID":41}

{"NAME":"ARROW_SUB_SCAN","ID":42}

{"NAME":"ARROW_WRITER","ID":43}

{"NAME":"JSON_WRITER","ID":44}

{"NAME":"VALUES_READER","ID":45}

{"NAME":"CONVERT_FROM_JSON","ID":46}

{"NAME":"JDBC_SUB_SCAN","ID":47}

{"NAME":"DICTIONARY_LOOKUP","ID":48}

{"NAME":"WRITER_COMMITTER","ID":49}

{"NAME":"ROUND_ROBIN_SENDER","ID":50}

效果

参考查询

• 查询行数分析

SELECT majorFragmentId, operatorId, op_type.name, sum(records) records

-- minorFragmentId, nested_4.minorFragmentProfile.maxMemoryUsed AS maxMemoryUsed, nested_4.minorFragmentProfile.memoryUsed AS memoryUsed, nested_4.endpoint.address AS address, nested_4.endpoint.maxDirectMemory AS maxDirectMemory

FROM (

  SELECT minorFragmentProfile, nested_3.minorFragmentProfile.endpoint AS endpoint, minorFragmentId, operatorType, operatorId, nested_3.inputProfile.records AS records, majorFragmentId

  FROM (

    SELECT minorFragmentProfile, nested_2.minorFragmentProfile.minorFragmentId AS minorFragmentId, operatorProfile, nested_2.operatorProfile.operatorType AS operatorType, nested_2.operatorProfile.operatorId AS operatorId, flatten(nested_2.operatorProfile.inputProfile) AS inputProfile, majorFragmentId

    FROM (

      SELECT fragmentProfile, minorFragmentProfile, flatten(nested_1.minorFragmentProfile.operatorProfile) AS operatorProfile, majorFragmentId

      FROM (

        SELECT fragmentProfile, flatten(nested_0.fragmentProfile.minorFragmentProfile) AS minorFragmentProfile, nested_0.fragmentProfile.majorFragmentId AS majorFragmentId

        FROM (

          SELECT flatten(fragmentProfile) AS fragmentProfile

          FROM "@dalong"."profile_attempt_0" AS "profile_attempt_0"

        ) nested_0

      ) nested_1

    ) nested_2

  ) nested_3

) nested_4, "@dalong".operator_type op_type

where nested_4.operatorType = op_type.id

group by majorFragmentId, operatorId, op_type.name

order by majorFragmentId, operatorId

效果

• 查询内存情况

SELECT nested_2.endpoint.address AS address, sum(nested_2.minorFragmentProfile.maxMemoryUsed)/1024/1024 AS maxMemoryUsed_MB

-- nested_2.minorFragmentProfile.memoryUsed AS memoryUsed, nested_2.minorFragmentProfile.maxMemoryUsed AS maxMemoryUsed, nested_2.endpoint.address AS address, nested_2.endpoint.maxDirectMemory AS maxDirectMemory, nested_2.endpoint.provisionId AS provisionId, majorFragmentId

FROM (

 SELECT fragmentProfile, minorFragmentProfile, nested_1.minorFragmentProfile.endpoint AS endpoint, majorFragmentId

 FROM (

   SELECT fragmentProfile, flatten(nested_0.fragmentProfile.minorFragmentProfile) AS minorFragmentProfile, nested_0.fragmentProfile.majorFragmentId AS majorFragmentId

   FROM (

     SELECT flatten(fragmentProfile) AS fragmentProfile

     FROM "@dalong"."profile_attempt_0" AS "profile_attempt_0"

   ) nested_0

 ) nested_1

) nested_2

group by nested_2.endpoint.address

order by nested_2.endpoint.address

效果

说明

对于job 的分析可以结合apache drill 学习，dremio 文档对于这部分介绍还是太简单，推荐结合drill 学习，同时默认32k 数据太小，需要调整下
参考命令

 

alter system set limits.single_field_size_bytes = 99000;

参考资料

https://docs.dremio.com/software/jobs/job-profile/
https://docs.dremio.com/software/jobs/analyzing-profiles/
https://docs.dremio.com/software/jobs/analyzing-profiles-attempt-0/
https://docs.dremio.com/software/jobs/analyzing-profiles-mappings-for-examples/
https://drill.apache.org/docs/query-profiles/
https://drill.apache.org/docs/query-profile-column-descriptions/