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Rw beam 9557 #11253
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Rw beam 9557 #11253
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* Add WithKeys lesson in Python Katas * Add Windowing Adding Timestamp section using ParDo lesson * Fix window import statement * Add Windowing Fixed Time Window lesson * Add WithKeys lesson in Python Katas * Add Windowing Adding Timestamp section using ParDo lesson * Fix window import statement * Add Windowing Fixed Time Window lesson * Update course on Stepik * Add missing license header
[BEAM-5898] Upgrading gRPC to 1.27
…EAD by default in SQL
…tegration with DirectRunner [BEAM-8335] TeststreamService integration with DirectRunner
Updating BigQuery client APIs
…dencies (apache#11093) * Exclude signature files from test dependencies * Apply suggestions from code review Thanks Luke! Co-Authored-By: Lukasz Cwik <lcwik@google.com> Co-authored-by: Lukasz Cwik <lcwik@google.com>
…le with Flink 1.10
…get and Make FlinkRunner compatible with Flink 1.10
[BEAM-8681] Upgrading grpc-google-cloud-pubsub-v1 to 1.85.1
The cleanup code in DefaultJobBundleFactory and its RemoteEnvironments may leak resources. This is especially a concern when the execution engines reuses the same JVM or underlying machines for multiple runs of a pipeline. Exceptions encountered during cleanup should not lead to aborting the cleanup procedure. Not all code handles this correctly. We should also ensure that the cleanup succeeds even if the runner does not properly close the bundle, e.g. when a exception occurs during closing the bundle.
[BEAM-9074] Update Python roadmap for 2.7 eol
…d_gbk_side_input [BEAM-9402] Remove options overwrite
Change-Id: Ib61aa3fac53d9109178744e11eeebe5c5da0929c
…ndleFactory and ProcessEnvironment
…Cache to subclass the CacheManager [BEAM-8335] Modify the StreamingCache to subclass the CacheManager
…ache#11106) * Fixing apache_beam.io.gcp.bigquery_test:PubSubBigQueryIT. at head (apache#11106) * Adding flink 1.10 to pipelineoptions
[BEAM-9731] Add SideInputLoadTest implementation
… holds Benchmarks have shown that the minimum output timestamp computation over all timers is very expensive because we have to iterate over all keys in the state backend. This is especially costly for RocksDB. This change introduces a cache which stores and updates the timer output timestamps such that the minimum can be retrieved efficiently. Only at startup the cache is populated from the state backend. This should give us roughly the same performance as before introducing output timestamps for timers. The following have been run with RocksDB: Nexmark Query 12 before: ``` Conf Runtime(sec) (Baseline) Events(/sec) (Baseline) Results (Baseline) 0000 105.3 950.0 9039 0001 2.2 4466.3 196 ``` Nexmark query 12 with this change: ``` Performance: Conf Runtime(sec) (Baseline) Events(/sec) (Baseline) Results (Baseline) 0000 6.0 16730.8 1919 0001 0.8 12391.6 196 ``` For more details please see BEAM-9566.
Reenable test_streaming_wordcount_it Rename the test in streaming_wordcount_debugging_it_test.py
[BEAM-9340] Validate pipeline requirements in PipelineValidator.
…ue for output timestamp watermark holds
…nvironment capabilities.
…ordcount_debugging_it
pyarrow was released on Feb 2020.
[BEAM-9562] Remove TimerSpec from Proto
…Option to setOption on Option builder [BEAM-9605] BIP-1: Rename setRowOption to setOption on Option builder
Added missing rename.
…Option to setOption on Option builder [BEAM-9605] BIP-1: Rename setRowOption to setOption on Option builder
Update the range for pyarrow
O actually this is not right way to create cherry-pick PR. closing now. |
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R @aaltay
cherry-pick #11252
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).[BEAM-XXX] Fixes bug in ApproximateQuantiles
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