This is an automated email from the ASF dual-hosted git repository. xxyu pushed a commit to branch document in repository https://gitbox.apache.org/repos/asf/kylin.git
commit 02182d1abf9f72b28649b8752dfbbee0095acf72 Author: xuekaiqi <[email protected]> AuthorDate: Tue Nov 10 15:24:17 2020 +0800 a little update --- website/_docs/tutorial/setup_systemcube.cn.md | 100 +++++++++++++------------- website/_docs/tutorial/setup_systemcube.md | 98 +++++++++++++------------ 2 files changed, 103 insertions(+), 95 deletions(-) diff --git a/website/_docs/tutorial/setup_systemcube.cn.md b/website/_docs/tutorial/setup_systemcube.cn.md index d05e942..86a3cdb 100644 --- a/website/_docs/tutorial/setup_systemcube.cn.md +++ b/website/_docs/tutorial/setup_systemcube.cn.md @@ -20,7 +20,7 @@ permalink: /cn/docs/tutorial/setup_systemcube.html ## <span id="如何建立系统 Cube">如何建立系统 Cube</span> -本节我们介绍手动启用系统 Cube 的方法，如果您希望通过 shell 脚本自动创建系统 Cube，请参考[自动创建系统 Cube](#什么是系统 Cube)。 +本节我们介绍手动启用系统 Cube 的方法，如果您希望通过 shell 脚本自动创建系统 Cube，请参考[自动创建系统 Cube](#自动创建系统 Cube)。 ### 1. 准备 @@ -173,7 +173,7 @@ Hive 中有 5 张表记录了 Kylin 系统的相关指标数据，每一个系 </tr> <tr> <td>REALIZATION</td> - <td>the cube which the query hits. In Kylin，there are two OLAP realizations: Cube，or Hybrid of Cubes</td> + <td>the cube which the query hits. In Kylin, there are two OLAP realizations: Cube, or Hybrid of Cubes</td> </tr> <tr> <td>REALIZATION_TYPE</td> @@ -181,11 +181,11 @@ Hive 中有 5 张表记录了 Kylin 系统的相关指标数据，每一个系 </tr> <tr> <td>QUERY_TYPE</td> - <td>users can query on different data sources，CACHE，OLAP，LOOKUP_TABLE，HIVE</td> + <td>users can query on different data sources: CACHE, OLAP, LOOKUP_TABLE, HIVE</td> </tr> <tr> <td>EXCEPTION</td> - <td>when doing query，exceptions may happen. It's for classifying different exception types</td> + <td>when doing query, exceptions may happen. It's for classifying different exception types</td> </tr> </table> @@ -198,19 +198,19 @@ Hive 中有 5 张表记录了 Kylin 系统的相关指标数据，每一个系 <td></td> </tr> <tr> - <td>MIN，MAX，SUM，PERCENTILE_APPROX of QUERY_TIME_COST</td> + <td>MIN, MAX, SUM, PERCENTILE_APPROX of QUERY_TIME_COST</td> <td>the time cost for the whole query</td> </tr> <tr> - <td>MAX，SUM of CALCITE_SIZE_RETURN</td> + <td>MAX, SUM of CALCITE_SIZE_RETURN</td> <td>the row count of the result Calcite returns</td> </tr> <tr> - <td>MAX，SUM of STORAGE_SIZE_RETURN</td> + <td>MAX, SUM of STORAGE_SIZE_RETURN</td> <td>the row count of the input to Calcite</td> </tr> <tr> - <td>MAX，SUM of CALCITE_SIZE_AGGREGATE_FILTER</td> + <td>MAX, SUM of CALCITE_SIZE_AGGREGATE_FILTER</td> <td>the row count of Calcite aggregates and filters</td> </tr> <tr> @@ -220,6 +220,7 @@ Hive 中有 5 张表记录了 Kylin 系统的相关指标数据，每一个系 </table> + ### METRICS_QUERY_RPC 这个 Cube 用于在最低级别收集查询 metrics。对于一个查询，相关的 aggregation 和 filter 能够下推到每一个 rpc 目标服务器。Rpc 目标服务器的健壮性是更好查询性能的基础。细节如下： @@ -237,7 +238,7 @@ Hive 中有 5 张表记录了 Kylin 系统的相关指标数据，每一个系 </tr> <tr> <td>REALIZATION</td> - <td>the cube which the query hits.</td> + <td>the cube which the query hits</td> </tr> <tr> <td>RPC_SERVER</td> @@ -245,7 +246,7 @@ Hive 中有 5 张表记录了 Kylin 系统的相关指标数据，每一个系 </tr> <tr> <td>EXCEPTION</td> - <td>the exception of a rpc call. If no exception，"NULL" is used</td> + <td>the exception of a rpc call. If no exception, "NULL" is used</td> </tr> </table> @@ -258,32 +259,33 @@ Hive 中有 5 张表记录了 Kylin 系统的相关指标数据，每一个系 <td></td> </tr> <tr> - <td>MAX，SUM，PERCENTILE_APPROX of CALL_TIME</td> + <td>MAX, SUM, PERCENTILE_APPROX of CALL_TIME</td> <td>the time cost of a rpc all</td> </tr> <tr> - <td>MAX，SUM of COUNT_SKIP</td> - <td>based on fuzzy filters or else，a few rows will be skiped. This indicates the skipped row count</td> + <td>MAX, SUM of COUNT_SKIP</td> + <td>based on fuzzy filters or else, a few rows will be skiped. This indicates the skipped row count</td> </tr> <tr> - <td>MAX，SUM of SIZE_SCAN</td> + <td>MAX, SUM of SIZE_SCAN</td> <td>the row count actually scanned</td> </tr> <tr> - <td>MAX，SUM of SIZE_RETURN</td> + <td>MAX, SUM of SIZE_RETURN</td> <td>the row count actually returned</td> </tr> <tr> - <td>MAX，SUM of SIZE_AGGREGATE</td> + <td>MAX, SUM of SIZE_AGGREGATE</td> <td>the row count actually aggregated</td> </tr> <tr> - <td>MAX，SUM of SIZE_AGGREGATE_FILTER</td> - <td>the row count actually aggregated and filtered，= SIZE_SCAN - SIZE_RETURN</td> + <td>MAX, SUM of SIZE_AGGREGATE_FILTER</td> + <td>the row count actually aggregated and filtered, = SIZE_SCAN - SIZE_RETURN</td> </tr> </table> + ### METRICS_QUERY_CUBE 这个 Cube 用于在 Cube 级别收集查询 metrics。最重要的是 cuboids 相关的，其为 Cube planner 提供服务。细节如下： @@ -316,7 +318,6 @@ Hive 中有 5 张表记录了 Kylin 系统的相关指标数据，每一个系 <td>whether a query on this Cube is successful or not</td> </tr> </table> - <table> <tr> <th colspan="2">Measure</th> @@ -330,40 +331,41 @@ Hive 中有 5 张表记录了 Kylin 系统的相关指标数据，每一个系 <td></td> </tr> <tr> - <td>MAX，SUM of STORAGE_CALL_COUNT</td> + <td>MAX, SUM of STORAGE_CALL_COUNT</td> <td>the number of rpc calls for a query hit on this Cube</td> </tr> <tr> - <td>MAX，SUM of STORAGE_CALL_TIME_SUM</td> + <td>MAX, SUM of STORAGE_CALL_TIME_SUM</td> <td>sum of time cost for the rpc calls of a query</td> </tr> <tr> - <td>MAX，SUM of STORAGE_CALL_TIME_MAX</td> + <td>MAX, SUM of STORAGE_CALL_TIME_MAX</td> <td>max of time cost among the rpc calls of a query</td> </tr> <tr> - <td>MAX，SUM of STORAGE_COUNT_SKIP</td> + <td>MAX, SUM of STORAGE_COUNT_SKIP</td> <td>the sum of row count skipped for the related rpc calls</td> </tr> <tr> - <td>MAX，SUM of STORAGE_COUNT_SCAN</td> + <td>MAX, SUM of STORAGE_COUNT_SCAN</td> <td>the sum of row count scanned for the related rpc calls</td> </tr> <tr> - <td>MAX，SUM of STORAGE_COUNT_RETURN</td> + <td>MAX, SUM of STORAGE_COUNT_RETURN</td> <td>the sum of row count returned for the related rpc calls</td> </tr> <tr> - <td>MAX，SUM of STORAGE_COUNT_AGGREGATE</td> + <td>MAX, SUM of STORAGE_COUNT_AGGREGATE</td> <td>the sum of row count aggregated for the related rpc calls</td> </tr> <tr> - <td>MAX，SUM of STORAGE_COUNT_AGGREGATE_FILTER</td> - <td>the sum of row count aggregated and filtered for the related rpc calls，= STORAGE_SIZE_SCAN - STORAGE_SIZE_RETURN</td> + <td>MAX, SUM of STORAGE_COUNT_AGGREGATE_FILTER</td> + <td>the sum of row count aggregated and filtered for the related rpc calls, = STORAGE_SIZE_SCAN - STORAGE_SIZE_RETURN</td> </tr> </table> + ### METRICS_JOB 在 Kylin 中，主要有三种类型的 job： - "BUILD"，为了从 **HIVE** 中 building Cube segments。 @@ -378,19 +380,19 @@ Hive 中有 5 张表记录了 Kylin 系统的相关指标数据，每一个系 </tr> <tr> <td>HOST</td> - <td>the host of server for query engine</td> + <td>the host of server for job engine</td> </tr> <tr> <td>KUSER</td> - <td>the user who executes the query</td> + <td>the user who run the job</td> </tr> <tr> <td>PROJECT</td> - <td>the project where the query executes</td> + <td>the project where the job runs</td> </tr> <tr> <td>CUBE_NAME</td> - <td>the cube which the query hits.</td> + <td>the cube with which the job is related</td> </tr> <tr> <td>JOB_TYPE</td> @@ -398,7 +400,7 @@ Hive 中有 5 张表记录了 Kylin 系统的相关指标数据，每一个系 </tr> <tr> <td>CUBING_TYPE</td> - <td>in kylin，there are two cubing algorithms，Layered & Fast(InMemory)</td> + <td>in kylin, there are two cubing algorithms, Layered & Fast(InMemory)</td> </tr> </table> @@ -411,44 +413,45 @@ Hive 中有 5 张表记录了 Kylin 系统的相关指标数据，每一个系 <td></td> </tr> <tr> - <td>MIN，MAX，SUM，PERCENTILE_APPROX of DURATION</td> + <td>MIN, MAX, SUM, PERCENTILE_APPROX of DURATION</td> <td>the duration from a job start to finish</td> </tr> <tr> - <td>MIN，MAX，SUM of TABLE_SIZE</td> + <td>MIN, MAX, SUM of TABLE_SIZE</td> <td>the size of data source in bytes</td> </tr> <tr> - <td>MIN，MAX，SUM of CUBE_SIZE</td> + <td>MIN, MAX, SUM of CUBE_SIZE</td> <td>the size of created Cube segment in bytes</td> </tr> <tr> - <td>MIN，MAX，SUM of PER_BYTES_TIME_COST</td> + <td>MIN, MAX, SUM of PER_BYTES_TIME_COST</td> <td>= DURATION / TABLE_SIZE</td> </tr> <tr> - <td>MIN，MAX，SUM of WAIT_RESOURCE_TIME</td> + <td>MIN, MAX, SUM of WAIT_RESOURCE_TIME</td> <td>a job may includes serveral MR(map reduce) jobs. Those MR jobs may wait because of lack of Hadoop resources.</td> </tr> <tr> - <td>MAX，SUM of step_duration_distinct_columns</td> + <td>MAX, SUM of step_duration_distinct_columns</td> <td></td> </tr> <tr> - <td>MAX，SUM of step_duration_dictionary</td> + <td>MAX, SUM of step_duration_dictionary</td> <td></td> </tr> <tr> - <td>MAX，SUM of step_duration_inmem_cubing</td> + <td>MAX, SUM of step_duration_inmem_cubing</td> <td></td> </tr> <tr> - <td>MAX，SUM of step_duration_hfile_convert</td> + <td>MAX, SUM of step_duration_hfile_convert</td> <td></td> </tr> </table> + ### METRICS_JOB_EXCEPTION 这个 Cube 是用来收集 job exception 指标。细节如下： @@ -458,19 +461,19 @@ Hive 中有 5 张表记录了 Kylin 系统的相关指标数据，每一个系 </tr> <tr> <td>HOST</td> - <td>the host of server for query engine</td> + <td>the host of server for job engine</td> </tr> <tr> <td>KUSER</td> - <td>the user who executes the query</td> + <td>the user who run a job</td> </tr> <tr> <td>PROJECT</td> - <td>the project where the query executes</td> + <td>the project where the job runs</td> </tr> <tr> <td>CUBE_NAME</td> - <td>the cube which the query hits.</td> + <td>the cube with which the job is related</td> </tr> <tr> <td>JOB_TYPE</td> @@ -478,15 +481,16 @@ Hive 中有 5 张表记录了 Kylin 系统的相关指标数据，每一个系 </tr> <tr> <td>CUBING_TYPE</td> - <td>in kylin，there are two cubing algorithms，Layered & Fast(InMemory)</td> + <td>in kylin, there are two cubing algorithms, Layered & Fast(InMemory)</td> </tr> <tr> <td>EXCEPTION</td> - <td>when running a job，exceptions may happen. It's for classifying different exception types</td> + <td>when running a job, exceptions may happen. It's for classifying different exception types</td> </tr> </table> + <table> <tr> <th>Measure</th> diff --git a/website/_docs/tutorial/setup_systemcube.md b/website/_docs/tutorial/setup_systemcube.md index 196aca4..70cecad 100644 --- a/website/_docs/tutorial/setup_systemcube.md +++ b/website/_docs/tutorial/setup_systemcube.md @@ -20,7 +20,7 @@ For better supporting self-monitoring, a set of system Cubes are created under t ## <span id="How to Set Up System Cube">How to Set Up System Cube</span> -In this section, we will introduce the method of manually enabling the system cube. If you want to automatically enable the system cube through shell scripts, please refer to [Automatically Create System Cube](#What is System Cube). +In this section, we will introduce the method of manually enabling the system cube. If you want to automatically enable the system cube through shell scripts, please refer to [Automatically Create System Cube](#Automatically Create System Cube). ### 1. Prepare @@ -176,7 +176,7 @@ This Cube is for collecting query metrics at the highest level. The details are </tr> <tr> <td>REALIZATION</td> - <td>the cube which the query hits. In Kylin，there are two OLAP realizations: Cube，or Hybrid of Cubes</td> + <td>the cube which the query hits. In Kylin, there are two OLAP realizations: Cube, or Hybrid of Cubes</td> </tr> <tr> <td>REALIZATION_TYPE</td> @@ -184,14 +184,15 @@ This Cube is for collecting query metrics at the highest level. The details are </tr> <tr> <td>QUERY_TYPE</td> - <td>users can query on different data sources，CACHE，OLAP，LOOKUP_TABLE，HIVE</td> + <td>users can query on different data sources: CACHE, OLAP, LOOKUP_TABLE, HIVE</td> </tr> <tr> <td>EXCEPTION</td> - <td>when doing query，exceptions may happen. It's for classifying different exception types</td> + <td>when doing query, exceptions may happen. It's for classifying different exception types</td> </tr> </table> + <table> <tr> <th colspan="2">Measure</th> @@ -201,19 +202,19 @@ This Cube is for collecting query metrics at the highest level. The details are <td></td> </tr> <tr> - <td>MIN，MAX，SUM，PERCENTILE_APPROX of QUERY_TIME_COST</td> + <td>MIN, MAX, SUM, PERCENTILE_APPROX of QUERY_TIME_COST</td> <td>the time cost for the whole query</td> </tr> <tr> - <td>MAX，SUM of CALCITE_SIZE_RETURN</td> + <td>MAX, SUM of CALCITE_SIZE_RETURN</td> <td>the row count of the result Calcite returns</td> </tr> <tr> - <td>MAX，SUM of STORAGE_SIZE_RETURN</td> + <td>MAX, SUM of STORAGE_SIZE_RETURN</td> <td>the row count of the input to Calcite</td> </tr> <tr> - <td>MAX，SUM of CALCITE_SIZE_AGGREGATE_FILTER</td> + <td>MAX, SUM of CALCITE_SIZE_AGGREGATE_FILTER</td> <td>the row count of Calcite aggregates and filters</td> </tr> <tr> @@ -240,7 +241,7 @@ This Cube is for collecting query metrics at the lowest level. For a query, the </tr> <tr> <td>REALIZATION</td> - <td>the cube which the query hits.</td> + <td>the cube which the query hits</td> </tr> <tr> <td>RPC_SERVER</td> @@ -261,32 +262,33 @@ This Cube is for collecting query metrics at the lowest level. For a query, the <td></td> </tr> <tr> - <td>MAX，SUM，PERCENTILE_APPROX of CALL_TIME</td> + <td>MAX, SUM, PERCENTILE_APPROX of CALL_TIME</td> <td>the time cost of a rpc all</td> </tr> <tr> - <td>MAX，SUM of COUNT_SKIP</td> - <td>based on fuzzy filters or else，a few rows will be skiped. This indicates the skipped row count</td> + <td>MAX, SUM of COUNT_SKIP</td> + <td>based on fuzzy filters or else, a few rows will be skiped. This indicates the skipped row count</td> </tr> <tr> - <td>MAX，SUM of SIZE_SCAN</td> + <td>MAX, SUM of SIZE_SCAN</td> <td>the row count actually scanned</td> </tr> <tr> - <td>MAX，SUM of SIZE_RETURN</td> + <td>MAX, SUM of SIZE_RETURN</td> <td>the row count actually returned</td> </tr> <tr> - <td>MAX，SUM of SIZE_AGGREGATE</td> + <td>MAX, SUM of SIZE_AGGREGATE</td> <td>the row count actually aggregated</td> </tr> <tr> - <td>MAX，SUM of SIZE_AGGREGATE_FILTER</td> - <td>the row count actually aggregated and filtered，= SIZE_SCAN - SIZE_RETURN</td> + <td>MAX, SUM of SIZE_AGGREGATE_FILTER</td> + <td>the row count actually aggregated and filtered, = SIZE_SCAN - SIZE_RETURN</td> </tr> </table> + ### METRICS_QUERY_CUBE This Cube is for collecting query metrics at the Cube level. The most important are cuboids related, which will serve for Cube planner. The details are as follows: @@ -319,7 +321,6 @@ This Cube is for collecting query metrics at the Cube level. The most important <td>whether a query on this Cube is successful or not</td> </tr> </table> - <table> <tr> <th colspan="2">Measure</th> @@ -333,40 +334,41 @@ This Cube is for collecting query metrics at the Cube level. The most important <td></td> </tr> <tr> - <td>MAX，SUM of STORAGE_CALL_COUNT</td> + <td>MAX, SUM of STORAGE_CALL_COUNT</td> <td>the number of rpc calls for a query hit on this Cube</td> </tr> <tr> - <td>MAX，SUM of STORAGE_CALL_TIME_SUM</td> + <td>MAX, SUM of STORAGE_CALL_TIME_SUM</td> <td>sum of time cost for the rpc calls of a query</td> </tr> <tr> - <td>MAX，SUM of STORAGE_CALL_TIME_MAX</td> + <td>MAX, SUM of STORAGE_CALL_TIME_MAX</td> <td>max of time cost among the rpc calls of a query</td> </tr> <tr> - <td>MAX，SUM of STORAGE_COUNT_SKIP</td> + <td>MAX, SUM of STORAGE_COUNT_SKIP</td> <td>the sum of row count skipped for the related rpc calls</td> </tr> <tr> - <td>MAX，SUM of STORAGE_COUNT_SCAN</td> + <td>MAX, SUM of STORAGE_COUNT_SCAN</td> <td>the sum of row count scanned for the related rpc calls</td> </tr> <tr> - <td>MAX，SUM of STORAGE_COUNT_RETURN</td> + <td>MAX, SUM of STORAGE_COUNT_RETURN</td> <td>the sum of row count returned for the related rpc calls</td> </tr> <tr> - <td>MAX，SUM of STORAGE_COUNT_AGGREGATE</td> + <td>MAX, SUM of STORAGE_COUNT_AGGREGATE</td> <td>the sum of row count aggregated for the related rpc calls</td> </tr> <tr> - <td>MAX，SUM of STORAGE_COUNT_AGGREGATE_FILTER</td> - <td>the sum of row count aggregated and filtered for the related rpc calls，= STORAGE_SIZE_SCAN - STORAGE_SIZE_RETURN</td> + <td>MAX, SUM of STORAGE_COUNT_AGGREGATE_FILTER</td> + <td>the sum of row count aggregated and filtered for the related rpc calls, = STORAGE_SIZE_SCAN - STORAGE_SIZE_RETURN</td> </tr> </table> + ### METRICS_JOB In Kylin, there are mainly three types of job: - "BUILD", for building Cube segments from **HIVE**. @@ -381,19 +383,19 @@ This Cube is for collecting job metrics. The details are as follows: </tr> <tr> <td>HOST</td> - <td>the host of server for query engine</td> + <td>the host of server for job engine</td> </tr> <tr> <td>KUSER</td> - <td>the user who executes the query</td> + <td>the user who run the job</td> </tr> <tr> <td>PROJECT</td> - <td>the project where the query executes</td> + <td>the project where the job runs</td> </tr> <tr> <td>CUBE_NAME</td> - <td>the cube which the query hits.</td> + <td>the cube with which the job is related</td> </tr> <tr> <td>JOB_TYPE</td> @@ -401,7 +403,7 @@ This Cube is for collecting job metrics. The details are as follows: </tr> <tr> <td>CUBING_TYPE</td> - <td>in kylin，there are two cubing algorithms，Layered & Fast(InMemory)</td> + <td>in kylin, there are two cubing algorithms, Layered & Fast(InMemory)</td> </tr> </table> @@ -414,44 +416,45 @@ This Cube is for collecting job metrics. The details are as follows: <td></td> </tr> <tr> - <td>MIN，MAX，SUM，PERCENTILE_APPROX of DURATION</td> + <td>MIN, MAX, SUM, PERCENTILE_APPROX of DURATION</td> <td>the duration from a job start to finish</td> </tr> <tr> - <td>MIN，MAX，SUM of TABLE_SIZE</td> + <td>MIN, MAX, SUM of TABLE_SIZE</td> <td>the size of data source in bytes</td> </tr> <tr> - <td>MIN，MAX，SUM of CUBE_SIZE</td> + <td>MIN, MAX, SUM of CUBE_SIZE</td> <td>the size of created Cube segment in bytes</td> </tr> <tr> - <td>MIN，MAX，SUM of PER_BYTES_TIME_COST</td> + <td>MIN, MAX, SUM of PER_BYTES_TIME_COST</td> <td>= DURATION / TABLE_SIZE</td> </tr> <tr> - <td>MIN，MAX，SUM of WAIT_RESOURCE_TIME</td> + <td>MIN, MAX, SUM of WAIT_RESOURCE_TIME</td> <td>a job may includes serveral MR(map reduce) jobs. Those MR jobs may wait because of lack of Hadoop resources.</td> </tr> <tr> - <td>MAX，SUM of step_duration_distinct_columns</td> + <td>MAX, SUM of step_duration_distinct_columns</td> <td></td> </tr> <tr> - <td>MAX，SUM of step_duration_dictionary</td> + <td>MAX, SUM of step_duration_dictionary</td> <td></td> </tr> <tr> - <td>MAX，SUM of step_duration_inmem_cubing</td> + <td>MAX, SUM of step_duration_inmem_cubing</td> <td></td> </tr> <tr> - <td>MAX，SUM of step_duration_hfile_convert</td> + <td>MAX, SUM of step_duration_hfile_convert</td> <td></td> </tr> </table> + ### METRICS_JOB_EXCEPTION This Cube is for collecting job exception metrics. The details are as follows: @@ -461,19 +464,19 @@ This Cube is for collecting job exception metrics. The details are as follows: </tr> <tr> <td>HOST</td> - <td>the host of server for query engine</td> + <td>the host of server for job engine</td> </tr> <tr> <td>KUSER</td> - <td>the user who executes the query</td> + <td>the user who run a job</td> </tr> <tr> <td>PROJECT</td> - <td>the project where the query executes</td> + <td>the project where the job runs</td> </tr> <tr> <td>CUBE_NAME</td> - <td>the cube which the query hits.</td> + <td>the cube with which the job is related</td> </tr> <tr> <td>JOB_TYPE</td> @@ -481,15 +484,16 @@ This Cube is for collecting job exception metrics. The details are as follows: </tr> <tr> <td>CUBING_TYPE</td> - <td>in kylin，there are two cubing algorithms，Layered & Fast(InMemory)</td> + <td>in kylin, there are two cubing algorithms, Layered & Fast(InMemory)</td> </tr> <tr> <td>EXCEPTION</td> - <td>when running a job，exceptions may happen. It's for classifying different exception types</td> + <td>when running a job, exceptions may happen. It's for classifying different exception types</td> </tr> </table> + <table> <tr> <th>Measure</th>
