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Copy pathspark_avro_to_parquet.py
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executable file
·124 lines (105 loc) · 4.99 KB
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#!/usr/bin/env python
# vim:ts=4:sts=4:sw=4:et
#
# Author: Hari Sekhon
# Date: 2015-11-03 21:38:52 +0000 (Tue, 03 Nov 2015)
#
# https://github.com/harisekhon/devops-python-tools
#
# License: see accompanying Hari Sekhon LICENSE file
#
# If you're using my code you're welcome to connect with me on LinkedIn and optionally send me feedback
# to help improve or steer this or other code I publish
#
# https://www.linkedin.com/in/harisekhon
#
"""
PySpark program to convert Avro file(s) to Parquet
Written to work across Python 2.x, supports Spark 1.4+
Tested on Spark 1.4.0, 1.5.1, 1.6.0, 1.6.2, 2.0.0 (requires spark-avro package 3.0.0+)
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# from __future__ import unicode_literals
import os
import sys
libdir = os.path.join(os.path.dirname(__file__), 'pylib')
sys.path.append(libdir)
try:
# pylint: disable=wrong-import-position
from harisekhon.utils import log, isMinVersion, support_msg, isVersionLax, die, getenv, pyspark_path
from harisekhon import CLI
except ImportError as _:
print('module import failed: %s' % _, file=sys.stderr)
print("Did you remember to build the project by running 'make'?", file=sys.stderr)
print("Alternatively perhaps you tried to copy this program out without it's adjacent libraries?", file=sys.stderr)
sys.exit(4)
# com.databricks:spark-avro_2.10:2.0.1 - 2.0.1 is for Spark 1.4+
# you can edit this bit if you need to run it on Spark 1.3:
# https://github.com/databricks/spark-avro#linking
# Must set spark-avro package to 3.0.0+ if using Spark 2.0
# for Spark < 2.0 it results in Exception:
# Caused by: java.lang.ClassNotFoundException: org.apache.spark.sql.execution.datasources.FileFormat
#os.environ['PYSPARK_SUBMIT_ARGS'] = '--packages com.databricks:spark-avro_2.10:3.0.0 %s' \
os.environ['PYSPARK_SUBMIT_ARGS'] = '--packages com.databricks:spark-avro_2.10:2.0.1 %s' \
% os.getenv('PYSPARK_SUBMIT_ARGS', '')
pyspark_path()
from pyspark import SparkContext # pylint: disable=wrong-import-position,import-error
from pyspark import SparkConf # pylint: disable=wrong-import-position,import-error
from pyspark.sql import SQLContext # pylint: disable=wrong-import-position,import-error
__author__ = 'Hari Sekhon'
__version__ = '0.8.0'
class SparkAvroToParquet(CLI):
def __init__(self):
# Python 2.x
super(SparkAvroToParquet, self).__init__()
# Python 3.x
# super().__init__()
# logging.config.fileConfig(os.path.join(libdir, 'resources', 'logging.conf'))
# log = logging.getLogger(self.__class__.__name__)
self.verbose_default = 2
self.timeout_default = 86400
# @override
def add_options(self):
self.add_opt('-a', '--avro', metavar='<file/dir>',
help='Avro input file/dir ($AVRO)',
default=getenv('AVRO'))
self.add_opt('-p', '--parquet-dir', metavar='<dir>',
help='Parquet output dir ($PARQUETDIR)',
default=getenv('PARQUETDIR'))
def parse_args(self):
self.no_args()
if not self.get_opt('avro'):
self.usage('--avro not defined')
if not self.get_opt('parquet_dir'):
self.usage('--parquet-dir not defined')
def run(self):
avro_file = self.get_opt('avro')
parquet_dir = self.get_opt('parquet_dir')
# let Spark fail if avro/parquet aren't available
# can't check paths exist as want to remain generically portable
# to HDFS, local filesystm or any other uri scheme Spark supports
log.info("Avro Source: %s" % avro_file)
log.info("Parquet Destination: %s" % parquet_dir)
conf = SparkConf().setAppName('HS PySpark Avro => Parquet')
sc = SparkContext(conf=conf) # pylint: disable=invalid-name
if self.verbose < 3 and 'setLogLevel' in dir(sc):
sc.setLogLevel('WARN')
sqlContext = SQLContext(sc) # pylint: disable=invalid-name
spark_version = sc.version
log.info('Spark version detected as %s' % spark_version)
if not isVersionLax(spark_version):
die("Spark version couldn't be determined. " + support_msg('pytools'))
# pylint: disable=invalid-name
if isMinVersion(spark_version, 1.4):
# this doesn't work in Spark <= 1.3 - github docs don't mention the older .method() for reading avro
df = sqlContext.read.format('com.databricks.spark.avro').load(avro_file)
df.write.parquet(parquet_dir)
else:
die('Spark <= 1.3 is not supported due to avro dependency, sorry! ' + \
'I may change this on request but prefer people just upgrade')
# log.warn('running legacy code for Spark <= 1.3')
# df.saveAsParquetFile(parquet_dir)
if __name__ == '__main__':
SparkAvroToParquet().main()