2cb976355c
## What changes were proposed in this pull request? Currently, the micro-batches in the MicroBatchExecution is not exposed to the user through any public API. This was because we did not want to expose the micro-batches, so that all the APIs we expose, we can eventually support them in the Continuous engine. But now that we have better sense of buiding a ContinuousExecution, I am considering adding APIs which will run only the MicroBatchExecution. I have quite a few use cases where exposing the microbatch output as a dataframe is useful. - Pass the output rows of each batch to a library that is designed only the batch jobs (example, uses many ML libraries need to collect() while learning). - Reuse batch data sources for output whose streaming version does not exists (e.g. redshift data source). - Writer the output rows to multiple places by writing twice for each batch. This is not the most elegant thing to do for multiple-output streaming queries but is likely to be better than running two streaming queries processing the same data twice. The proposal is to add a method `foreachBatch(f: Dataset[T] => Unit)` to Scala/Java/Python `DataStreamWriter`. ## How was this patch tested? New unit tests. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #21571 from tdas/foreachBatch.
176 lines
6.2 KiB
Python
176 lines
6.2 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import py4j
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class CapturedException(Exception):
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def __init__(self, desc, stackTrace):
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self.desc = desc
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self.stackTrace = stackTrace
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def __str__(self):
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return repr(self.desc)
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class AnalysisException(CapturedException):
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"""
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Failed to analyze a SQL query plan.
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"""
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class ParseException(CapturedException):
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"""
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Failed to parse a SQL command.
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"""
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class IllegalArgumentException(CapturedException):
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"""
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Passed an illegal or inappropriate argument.
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"""
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class StreamingQueryException(CapturedException):
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"""
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Exception that stopped a :class:`StreamingQuery`.
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"""
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class QueryExecutionException(CapturedException):
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"""
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Failed to execute a query.
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"""
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def capture_sql_exception(f):
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def deco(*a, **kw):
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try:
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return f(*a, **kw)
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except py4j.protocol.Py4JJavaError as e:
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s = e.java_exception.toString()
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stackTrace = '\n\t at '.join(map(lambda x: x.toString(),
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e.java_exception.getStackTrace()))
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if s.startswith('org.apache.spark.sql.AnalysisException: '):
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raise AnalysisException(s.split(': ', 1)[1], stackTrace)
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if s.startswith('org.apache.spark.sql.catalyst.analysis'):
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raise AnalysisException(s.split(': ', 1)[1], stackTrace)
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if s.startswith('org.apache.spark.sql.catalyst.parser.ParseException: '):
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raise ParseException(s.split(': ', 1)[1], stackTrace)
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if s.startswith('org.apache.spark.sql.streaming.StreamingQueryException: '):
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raise StreamingQueryException(s.split(': ', 1)[1], stackTrace)
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if s.startswith('org.apache.spark.sql.execution.QueryExecutionException: '):
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raise QueryExecutionException(s.split(': ', 1)[1], stackTrace)
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if s.startswith('java.lang.IllegalArgumentException: '):
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raise IllegalArgumentException(s.split(': ', 1)[1], stackTrace)
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raise
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return deco
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def install_exception_handler():
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"""
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Hook an exception handler into Py4j, which could capture some SQL exceptions in Java.
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When calling Java API, it will call `get_return_value` to parse the returned object.
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If any exception happened in JVM, the result will be Java exception object, it raise
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py4j.protocol.Py4JJavaError. We replace the original `get_return_value` with one that
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could capture the Java exception and throw a Python one (with the same error message).
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It's idempotent, could be called multiple times.
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"""
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original = py4j.protocol.get_return_value
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# The original `get_return_value` is not patched, it's idempotent.
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patched = capture_sql_exception(original)
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# only patch the one used in py4j.java_gateway (call Java API)
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py4j.java_gateway.get_return_value = patched
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def toJArray(gateway, jtype, arr):
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"""
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Convert python list to java type array
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:param gateway: Py4j Gateway
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:param jtype: java type of element in array
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:param arr: python type list
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"""
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jarr = gateway.new_array(jtype, len(arr))
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for i in range(0, len(arr)):
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jarr[i] = arr[i]
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return jarr
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def require_minimum_pandas_version():
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""" Raise ImportError if minimum version of Pandas is not installed
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"""
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# TODO(HyukjinKwon): Relocate and deduplicate the version specification.
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minimum_pandas_version = "0.19.2"
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from distutils.version import LooseVersion
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try:
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import pandas
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have_pandas = True
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except ImportError:
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have_pandas = False
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if not have_pandas:
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raise ImportError("Pandas >= %s must be installed; however, "
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"it was not found." % minimum_pandas_version)
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if LooseVersion(pandas.__version__) < LooseVersion(minimum_pandas_version):
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raise ImportError("Pandas >= %s must be installed; however, "
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"your version was %s." % (minimum_pandas_version, pandas.__version__))
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def require_minimum_pyarrow_version():
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""" Raise ImportError if minimum version of pyarrow is not installed
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"""
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# TODO(HyukjinKwon): Relocate and deduplicate the version specification.
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minimum_pyarrow_version = "0.8.0"
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from distutils.version import LooseVersion
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try:
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import pyarrow
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have_arrow = True
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except ImportError:
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have_arrow = False
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if not have_arrow:
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raise ImportError("PyArrow >= %s must be installed; however, "
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"it was not found." % minimum_pyarrow_version)
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if LooseVersion(pyarrow.__version__) < LooseVersion(minimum_pyarrow_version):
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raise ImportError("PyArrow >= %s must be installed; however, "
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"your version was %s." % (minimum_pyarrow_version, pyarrow.__version__))
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class ForeachBatchFunction(object):
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"""
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This is the Python implementation of Java interface 'ForeachBatchFunction'. This wraps
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the user-defined 'foreachBatch' function such that it can be called from the JVM when
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the query is active.
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"""
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def __init__(self, sql_ctx, func):
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self.sql_ctx = sql_ctx
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self.func = func
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def call(self, jdf, batch_id):
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from pyspark.sql.dataframe import DataFrame
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try:
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self.func(DataFrame(jdf, self.sql_ctx), batch_id)
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except Exception as e:
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self.error = e
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raise e
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class Java:
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implements = ['org.apache.spark.sql.execution.streaming.sources.PythonForeachBatchFunction']
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