可以再尝试下最新的1.11.2吗？
https://flink.apache.org/downloads.html
On Thu, Sep 17, 2020 at 1:33 PM kandy.wang <kandy1...@163.com> wrote:
> 是master分支代码
> 那你说的这个情况，刚好是table.exec.hive.fallback-mapred-writer默认是true 的情况
> 出现的，现在改成false 就走到else 部分 就暂时没这个问题了
> if (userMrWriter) {
> builder = bucketsBuilderForMRWriter(recordWriterFactory, sd, assigner,
> rollingPolicy, outputFileConfig);
> LOG.info("Hive streaming sink: Use MapReduce RecordWriter writer.");
> } else {
> Optional<BulkWriter.Factory<RowData>> bulkFactory =
> createBulkWriterFactory(partitionColumns, sd);
> if (bulkFactory.isPresent()) {
> builder = StreamingFileSink.forBulkFormat(
> new org.apache.flink.core.fs.Path(sd.getLocation()),
> new
> FileSystemTableSink.ProjectionBulkFactory(bulkFactory.get(), partComputer))
> .withBucketAssigner(assigner)
> .withRollingPolicy(rollingPolicy)
> .withOutputFileConfig(outputFileConfig);
> LOG.info("Hive streaming sink: Use native parquet&orc writer.");
> } else {
> builder = bucketsBuilderForMRWriter(recordWriterFactory, sd,
> assigner, rollingPolicy, outputFileConfig);
> LOG.info("Hive streaming sink: Use MapReduce RecordWriter writer because
> BulkWriter Factory not available.");
> }
> }
> 在 2020-09-17 13:21:40，"Jingsong Li" <jingsongl...@gmail.com> 写道：
> >是最新的代码吗？
> >1.11.2解了一个bug：https://issues.apache.org/jira/browse/FLINK-19121
> >它是影响性能的，1.11.2已经投票通过，即将发布
> >
> >On Thu, Sep 17, 2020 at 12:46 PM kandy.wang <kandy1...@163.com> wrote:
> >
> >> @Jingsong Li
> >>
> >> public TableSink createTableSink(TableSinkFactory.Context context) {
> >> CatalogTable table = checkNotNull(context.getTable());
> >> Preconditions.checkArgument(table instanceof CatalogTableImpl);
> >>
> >> boolean isGeneric =
> >>
> Boolean.parseBoolean(table.getProperties().get(CatalogConfig.IS_GENERIC));
> >>
> >> if (!isGeneric) {
> >> return new HiveTableSink(
> >> context.getConfiguration().get(
> >> HiveOptions.TABLE_EXEC_HIVE_FALLBACK_MAPRED_WRITER),
> >> context.isBounded(),
> >> new JobConf(hiveConf),
> >> context.getObjectIdentifier(),
> >> table);
> >> } else {
> >> return TableFactoryUtil.findAndCreateTableSink(context);
> >> }
> >> }
> >>
> >>
> HiveTableFactory中，有个配置table.exec.hive.fallback-mapred-writer默认是true，控制是否使用Hadoop
> >> 自带的mr writer还是用flink native 实现的 writer去写orc parquet格式。
> >>
> >> If it is false, using flink native writer to write parquet and orc
> files;
> >>
> >> If it is true, using hadoop mapred record writer to write parquet and
> orc
> >> files
> >>
> >> 将此参数调整成false后，同样的资源配置下，tps达到30W
> >>
> >> 这个不同的ORC实现，可能性能本身就存在差异吧？ 另外我们的存储格式是orc，orc有没有一些可以优化的参数，async flush
> >> 一些相关的参数 ？
> >>
> >>
> >>
> >>
> >>
> >> 在 2020-09-17 11:21:43，"Jingsong Li" <jingsongl...@gmail.com> 写道：
> >> >Sink并行度
> >> >我理解是配置Sink并行度，这个一直在讨论，还没结论
> >> >
> >> >HDFS性能
> >> >具体可以看HDFS到底什么瓶颈，是网络还是请求数还是连接数还是磁盘IO
> >> >
> >> >On Wed, Sep 16, 2020 at 8:16 PM kandy.wang <kandy1...@163.com> wrote:
> >> >
> >> >> 场景很简单，就是kafka2hive
> >> >> --5min入仓Hive
> >> >>
> >> >> INSERT INTO hive.temp_.hive_5min
> >> >>
> >> >> SELECT
> >> >>
> >> >> arg_service,
> >> >>
> >> >> time_local
> >> >>
> >> >> .....
> >> >>
> >> >> FROM_UNIXTIME((UNIX_TIMESTAMP()/300 * 300) ,'yyyyMMdd'),
> >> >> FROM_UNIXTIME((UNIX_TIMESTAMP()/300 * 300) ,'HHmm') 5min产生一个分区
> >> >>
> >> >> FROM hive.temp_.kafka_source_pageview/*+ OPTIONS('
> properties.group.id
> >> '='kafka_hive_test',
> >> >> 'scan.startup.mode'='earliest-offset') */;
> >> >>
> >> >>
> >> >>
> >> >> --kafka source表定义
> >> >>
> >> >> CREATE TABLE hive.temp_vipflink.kafka_source_pageview (
> >> >>
> >> >> arg_service string COMMENT 'arg_service',
> >> >>
> >> >> ....
> >> >>
> >> >> )WITH (
> >> >>
> >> >> 'connector' = 'kafka',
> >> >>
> >> >> 'topic' = '...',
> >> >>
> >> >> 'properties.bootstrap.servers' = '...',
> >> >>
> >> >> 'properties.group.id' = 'flink_etl_kafka_hive',
> >> >>
> >> >> 'scan.startup.mode' = 'group-offsets',
> >> >>
> >> >> 'format' = 'json',
> >> >>
> >> >> 'json.fail-on-missing-field' = 'false',
> >> >>
> >> >> 'json.ignore-parse-errors' = 'true'
> >> >>
> >> >> );
> >> >> --sink hive表定义
> >> >> CREATE TABLE temp_vipflink.vipflink_dm_log_app_pageview_5min (
> >> >> ....
> >> >> )
> >> >> PARTITIONED BY (dt string , hm string) stored as orc location
> >> >> 'hdfs://ssdcluster/....._5min' TBLPROPERTIES(
> >> >> 'sink.partition-commit.trigger'='process-time',
> >> >> 'sink.partition-commit.delay'='0 min',
> >> >> 'sink.partition-commit.policy.class'='...CustomCommitPolicy',
> >> >>
> 'sink.partition-commit.policy.kind'='metastore,success-file,custom',
> >> >> 'sink.rolling-policy.check-interval' ='30s',
> >> >> 'sink.rolling-policy.rollover-interval'='10min',
> >> >> 'sink.rolling-policy.file-size'='128MB'
> >> >> );
> >> >> 初步看下来，感觉瓶颈在写hdfs，hdfs 这边已经是ssd hdfs了，kafka的分区数=40
> >> >> ，算子并行度=40，tps也就达到6-7万这样子，并行度放大，性能并无提升。
> >> >> 就是flink sql可以
> >> >>
> >>
> 改局部某个算子的并行度，想单独改一下StreamingFileWriter算子的并行度，有什么好的办法么？然后StreamingFileWriter
> >> >> 这块，有没有什么可以提升性能相关的优化参数？
> >> >>
> >> >>
> >> >>
> >> >>
> >> >> 在 2020-09-16 19:29:50，"Jingsong Li" <jingsongl...@gmail.com> 写道：
> >> >> >Hi,
> >> >> >
> >> >> >可以分享下具体的测试场景吗？有对比吗？比如使用手写的DataStream作业来对比下，性能的差距？
> >> >> >
> >> >> >另外，压测时是否可以看下jstack？
> >> >> >
> >> >> >Best,
> >> >> >Jingsong
> >> >> >
> >> >> >On Wed, Sep 16, 2020 at 2:03 PM kandy.wang <kandy1...@163.com>
> wrote:
> >> >> >
> >> >> >> 压测下来，发现streaming方式写入hive StreamingFileWriter ，在kafka partition=40
> >> >> ，source
> >> >> >> writer算子并行度 =40的情况下，kafka从头消费，tps只能达到 7w
> >> >> >> 想了解一下，streaming方式写Hive 这块有压测过么？性能能达到多少
> >> >> >
> >> >> >
> >> >> >
> >> >> >--
> >> >> >Best, Jingsong Lee
> >> >>
> >> >
> >> >
> >> >--
> >> >Best, Jingsong Lee
> >>
> >
> >
> >--
> >Best, Jingsong Lee
>
--
Best, Jingsong Lee
