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This change adds Impala DDL support for creation of AVRO tables. Additionally, it add Impala support for CREATE and ALTER SERDEPROPERTIES which are used when creating Avro backed tables. This syntax is not exactly the same as the Hive support since it introduces a new fileformat (AVROFILE) that implies the needed Serialization library, input format, and output format. Change-Id: I5047e419198a89599e9d014fdedfee1a20437a7d Reviewed-on: http://gerrit.ent.cloudera.com:8080/464 Reviewed-by: Lenni Kuff <lskuff@cloudera.com> Tested-by: Lenni Kuff <lskuff@cloudera.com>
209 lines
9.2 KiB
Python
Executable File
209 lines
9.2 KiB
Python
Executable File
#!/usr/bin/env python
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# Copyright (c) 2012 Cloudera, Inc. All rights reserved.
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#
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# This script is used to load the proper datasets for the specified workloads. It loads
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# all data via Hive except for parquet data which needs to be loaded via Impala.
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# Most ddl commands are executed by Impala.
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import collections
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import os
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import re
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import subprocess
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import sys
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import tempfile
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import time
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from itertools import product
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from optparse import OptionParser
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parser = OptionParser()
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parser.add_option("-e", "--exploration_strategy", dest="exploration_strategy",
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default="core",
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help="The exploration strategy for schema gen: 'core', "\
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"'pairwise', or 'exhaustive'")
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parser.add_option("--hive_warehouse_dir", dest="hive_warehouse_dir",
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default="/test-warehouse",
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help="The HDFS path to the base Hive test warehouse directory")
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parser.add_option("-w", "--workloads", dest="workloads",
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help="Comma-separated list of workloads to load data for. If 'all' is "\
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"specified then data for all workloads is loaded.")
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parser.add_option("-s", "--scale_factor", dest="scale_factor", default="",
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help="An optional scale factor to generate the schema for")
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parser.add_option("-f", "--force_reload", dest="force_reload", action="store_true",
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default=False, help='Skips HDFS exists check and reloads all tables')
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parser.add_option("--compute_stats", dest="compute_stats", action="store_true",
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default= False, help="Execute COMPUTE STATISTICS statements on the "\
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"tables that are loaded")
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parser.add_option("--impalad", dest="impala_shell_args", default="localhost:21000",
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help="Impala daemon to connect to")
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parser.add_option("--table_names", dest="table_names", default=None,
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help="Only load the specified tables - specified as a comma-seperated "\
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"list of base table names")
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parser.add_option("--table_formats", dest="table_formats", default=None,
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help="Override the test vectors and load using the specified table "\
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"formats. Ex. --table_formats=seq/snap/block,text/none")
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parser.add_option("--hdfs_namenode", dest="hdfs_namenode", default="localhost:20500",
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help="HDFS name node for Avro schema URLs, default localhost:20500")
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parser.add_option("--workload_dir", dest="workload_dir",
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default=os.environ['IMPALA_WORKLOAD_DIR'],
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help="Directory that contains Impala workloads")
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parser.add_option("--dataset_dir", dest="dataset_dir",
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default=os.environ['IMPALA_DATASET_DIR'],
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help="Directory that contains Impala datasets")
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options, args = parser.parse_args()
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WORKLOAD_DIR = options.workload_dir
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DATASET_DIR = options.dataset_dir
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TESTDATA_BIN_DIR = os.path.join(os.environ['IMPALA_HOME'], 'testdata/bin')
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AVRO_SCHEMA_DIR = "avro_schemas"
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GENERATE_SCHEMA_CMD = "generate-schema-statements.py --exploration_strategy=%s "\
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"--workload=%s --scale_factor=%s --verbose"
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HIVE_CMD = os.path.join(os.environ['HIVE_HOME'], 'bin/hive')
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HIVE_ARGS = "-hiveconf hive.root.logger=WARN,console -v"
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IMPALA_SHELL_CMD = os.path.join(os.environ['IMPALA_HOME'], 'bin/impala-shell.sh')
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HADOOP_CMD = os.path.join(os.environ['HADOOP_HOME'], 'bin/hadoop')
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def available_workloads(workload_dir):
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return [subdir for subdir in os.listdir(workload_dir)
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if os.path.isdir(os.path.join(workload_dir, subdir))]
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def validate_workloads(all_workloads, workloads):
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for workload in workloads:
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if workload not in all_workloads:
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print 'Workload \'%s\' not found in workload directory' % workload
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print 'Available workloads: ' + ', '.join(all_workloads)
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sys.exit(1)
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def exec_cmd(cmd, error_msg, expect_success=True):
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ret_val = subprocess.call(cmd, shell=True)
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if expect_success and ret_val != 0:
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print error_msg
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sys.exit(ret_val)
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return ret_val
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def exec_hive_query_from_file(file_name):
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hive_cmd = "%s %s -f %s" % (HIVE_CMD, HIVE_ARGS, file_name)
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print 'Executing Hive Command: ' + hive_cmd
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exec_cmd(hive_cmd, 'Error executing file from Hive: ' + file_name)
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def exec_hbase_query_from_file(file_name):
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hbase_cmd = "hbase shell %s" % file_name
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exec_cmd(hbase_cmd, 'Error executing hbase create commands')
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def exec_impala_query_from_file(file_name):
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impala_refresh_cmd = "%s --impalad=%s -q 'invalidate metadata'" %\
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(IMPALA_SHELL_CMD, options.impala_shell_args)
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impala_cmd = "%s --impalad=%s -f %s" %\
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(IMPALA_SHELL_CMD, options.impala_shell_args, file_name)
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# Refresh catalog before and after
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exec_cmd(impala_refresh_cmd, 'Error executing refresh from Impala.')
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print 'Executing Impala Command: ' + impala_cmd
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exec_cmd(impala_cmd, 'Error executing file from Impala: ' + file_name)
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exec_cmd(impala_refresh_cmd, 'Error executing refresh from Impala.')
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def exec_bash_script(file_name):
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bash_cmd = "bash %s" % file_name
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print 'Executing Bash Command: ' + bash_cmd
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exec_cmd(bash_cmd, 'Error bash script: ' + file_name)
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def generate_schema_statements(workload):
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generate_cmd = GENERATE_SCHEMA_CMD % (options.exploration_strategy, workload,
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options.scale_factor)
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if options.table_names:
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generate_cmd += " --table_names=%s" % options.table_names
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if options.force_reload:
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generate_cmd += " --force_reload"
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if options.table_formats:
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generate_cmd += " --table_formats=%s" % options.table_formats
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if options.hive_warehouse_dir is not None:
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generate_cmd += " --hive_warehouse_dir=%s" % options.hive_warehouse_dir
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if options.hdfs_namenode is not None:
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generate_cmd += " --hdfs_namenode=%s" % options.hdfs_namenode
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print 'Executing Generate Schema Command: ' + generate_cmd
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schema_cmd = os.path.join(TESTDATA_BIN_DIR, generate_cmd)
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error_msg = 'Error generating schema statements for workload: ' + workload
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exec_cmd(schema_cmd, error_msg)
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def get_dataset_for_workload(workload):
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dimension_file_name = os.path.join(WORKLOAD_DIR, workload,
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'%s_dimensions.csv' % workload)
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if not os.path.isfile(dimension_file_name):
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print 'Dimension file not found: ' + dimension_file_name
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sys.exit(1)
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with open(dimension_file_name, 'rb') as input_file:
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match = re.search('dataset:\s*([\w\-\.]+)', input_file.read())
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if match:
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return match.group(1)
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else:
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print 'Dimension file does not contain dataset for workload \'%s\'' % (workload)
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sys.exit(1)
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def copy_avro_schemas_to_hdfs(schemas_dir):
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"""Recursively copies all of schemas_dir to the test warehouse."""
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if not os.path.exists(schemas_dir):
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print 'Avro schema dir (%s) does not exist. Skipping copy to HDFS.' % schemas_dir
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return
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exec_hadoop_fs_cmd("-mkdir -p " + options.hive_warehouse_dir)
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exec_hadoop_fs_cmd("-put -f %s %s/" % (schemas_dir, options.hive_warehouse_dir))
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def exec_hadoop_fs_cmd(args, expect_success=True):
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cmd = "%s fs %s" % (HADOOP_CMD, args)
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print "Executing Hadoop command: " + cmd
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exec_cmd(cmd, "Error executing Hadoop command, exiting",
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expect_success=expect_success)
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if __name__ == "__main__":
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all_workloads = available_workloads(WORKLOAD_DIR)
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workloads = []
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if options.workloads is None:
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print "At least one workload name must be specified."
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parser.print_help()
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sys.exit(1)
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elif options.workloads == 'all':
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print 'Loading data for all workloads.'
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workloads = all_workloads
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else:
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workloads = options.workloads.split(",")
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validate_workloads(all_workloads, workloads)
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print 'Starting data load for the following workloads: ' + ', '.join(workloads)
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loading_time_map = collections.defaultdict(float)
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for workload in workloads:
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start_time = time.time()
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dataset = get_dataset_for_workload(workload)
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dataset_dir = os.path.join(DATASET_DIR, dataset)
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os.chdir(dataset_dir)
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generate_schema_statements(workload)
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copy_avro_schemas_to_hdfs(AVRO_SCHEMA_DIR)
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generated_hbase_file = 'load-%s-%s-hbase.create' % (workload,
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options.exploration_strategy)
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if os.path.exists(generated_hbase_file):
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exec_hbase_query_from_file(os.path.join(dataset_dir, generated_hbase_file))
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generated_impala_file = \
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'load-%s-%s-impala-generated.sql' % (workload, options.exploration_strategy)
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if os.path.exists(generated_impala_file):
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exec_impala_query_from_file(os.path.join(dataset_dir, generated_impala_file))
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generated_hive_file =\
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'load-%s-%s-hive-generated.sql' % (workload, options.exploration_strategy)
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if os.path.exists(generated_hive_file):
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exec_hive_query_from_file(os.path.join(dataset_dir, generated_hive_file))
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generated_impala_file = \
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'load-%s-%s-impala-load-generated.sql' % (workload, options.exploration_strategy)
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if os.path.exists(generated_impala_file):
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exec_impala_query_from_file(os.path.join(dataset_dir, generated_impala_file))
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loading_time_map[workload] = time.time() - start_time
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total_time = 0.0
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for workload, load_time in loading_time_map.iteritems():
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total_time += load_time
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print 'Data loading for workload \'%s\' completed in: %.2fs'\
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% (workload, load_time)
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print 'Total load time: %.2fs\n' % total_time
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