A few of the Hadoop, Spark & Linux tools. All programs have --help to list the available options.

For many more tools see Tools and the Advanced Nagios Plugins Collection which contains many Hadoop, NoSQL, Web and infrastructure tools and Nagios plugins.

Hari Sekhon

Big Data Contractor, United Kingdom

http://www.linkedin.com/in/harisekhon

The 'make' command will initialize my library submodule:

git clone https://github.com/harisekhon/pytools
cd pytools
make

Some Hadoop tools with require Jython, see Jython for Hadoop Utils for details.

All programs come with a --help switch which includes a program description and the list of command line options.

Some common options also support optional environment variables for convenience to reduce repeated --switch usage or to hide them from being exposed in the process list. These are indicated in the --help descriptions in brackets next to each option eg. $HOST or more specific ones with higher precedence like $AMBARI_HOST.

• ambari_blueprints.py - Ambari Blueprint tool using Ambari API to find and fetch all blueprints or a specific blueprint to local json files, blueprint an existing cluster, or create a new cluster using a blueprint. See adjacent ambari_blueprints directory for some blueprint templates
• hadoop_hdfs_time_block_reads.jy - Hadoop HDFS per-block read timing debugger with datanode and rack locations for a given file or directory tree. Reports the slowest Hadoop datanodes in descending order at the end
• hadoop_hdfs_files_native_checksums.jy - fetches native HDFS checksums for quicker file comparisons (about 100x faster than doing hdfs dfs -cat | md5sum)
• hadoop_hdfs_files_stats.jy - fetches HDFS file stats
• pig-text-to-elasticsearch.pig / pig-text-to-solr.pig - bulk indexes unstructured files in Hadoop to Elasticsearch or Solr/SolrCloud clusters
• pig_udfs.jy - Pig Jython UDFs for Hadoop
• ipython-notebook-pyspark.py - per-user authenticated IPython Notebook + PySpark integration to allow each user to auto-create their own password protected IPython Notebook running Spark
• spark_avro_to_parquet.py - PySpark Avro => Parquet converter
• spark_parquet_to_avro.py - PySpark Parquet => Avro converter
• spark_csv_to_avro.py - PySpark CSV => Avro converter, supports both inferred and explicit schemas
• spark_csv_to_parquet.py - PySpark CSV => Parquet converter, supports both inferred and explicit schemas
• spark_json_to_avro.py - PySpark JSON => Avro converter
• spark_json_to_parquet.py - PySpark JSON => Parquet converter
• validate_*.py - validate files, directory trees and/or standard input streams for the following file types: Avro, CSV, JSON, Parquet, XML, YAML. Directories are recursed, testing any files with relevant matching extensions (.avro, .csv, .json, .parquet, .xml, .yml/.yaml). validate_json.py supports both normal json files as well as json-doc-per-line files such as MongoDB or Hadoop json data files
• welcome.py - cool spinning welcome message greeting your username and showing last login time and user (there also a perl version in my Tools repo)

Enter the pytools directory and run git submodule init and git submodule update to fetch my library repo:

git clone https://github.com/harisekhon/pytools
cd pytools
git submodule init
git submodule update
pip install -r requirements.txt

The 3 Hadoop utility programs listed below require Jython (as well as Hadoop to be installed and correctly configured or course)

hadoop_hdfs_time_block_reads.jy
hadoop_hdfs_files_native_checksums.jy
hadoop_hdfs_files_stats.jy

Jython is a simple download and unpack and can be fetched from http://www.jython.org/downloads.html

Then add the Jython untarred directory to the $PATH or specify the /path/to/jythondir/bin/jython explicitly:

/path/to/jython-x.y.z/bin/jython -J-cp `hadoop classpath` hadoop_hdfs_time_block_reads.jy --help

The -J-cp `hadoop classpath` bit does the right thing in finding the Hadoop java classes required to use the Hadoop APIs.

Strict validations include host/domain/FQDNs using TLDs which are populated from the official IANA list is done via my PyLib library submodule - see there for details on configuring this to permit custom TLDs like .local or .intranet (both supported by default).

Run make update. This will git pull and then git submodule update which is necessary to pick up corresponding library updates.

If you update often and want to just quickly git pull + submodule update but skip rebuilding all those dependencies each time then run make update-no-recompile (will miss new library dependencies - do full make update if you encounter issues).

Patches, improvements and even general feedback are welcome in the form of GitHub pull requests and issue tickets.

Tools - Hadoop, NoSQL, Hive, Solr, Ambari, Web, Linux

The Advanced Nagios Plugins Collection - 220+ programs for Nagios monitoring your Hadoop & NoSQL clusters. Covers every Hadoop vendor's management API and every major NoSQL technology (HBase, Cassandra, MongoDB, Elasticsearch, Solr, Riak, Redis etc.) as well as traditional Linux and infrastructure.

PyLib - my personal python library leveraged in this repo as a submodule

Perl Lib - Perl version of above library

Spark Apps eg. Spark => Elasticsearch - Scala application to index from Spark to Elasticsearch. Used to index data in Hadoop clusters or local data via Spark standalone. This started as a Scala Spark port of pig-text-to-elasticsearch.pig from this repo.