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https://github.com/HariSekhon/DevOps-Python-tools

80+ DevOps & Data CLI Tools - AWS, GCP, GCF Python Cloud Functions, Log Anonymizer, Spark, Hadoop, HBase, Hive, Impala, Linux, Docker, Spark Data Converters & Validators (Avro/Parquet/JSON/CSV/INI/XML/YAML), Travis CI, AWS CloudFormation, Elasticsearch, Solr etc.
https://github.com/HariSekhon/DevOps-Python-tools

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80+ DevOps & Data CLI Tools - AWS, GCP, GCF Python Cloud Functions, Log Anonymizer, Spark, Hadoop, HBase, Hive, Impala, Linux, Docker, Spark Data Converters & Validators (Avro/Parquet/JSON/CSV/INI/XML/YAML), Travis CI, AWS CloudFormation, Elasticsearch, Solr etc.

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# Hari Sekhon - DevOps Python Tools



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[git.io/pytools](https://git.io/pytools)



## AWS, Docker, Spark, Hadoop, HBase, Hive, Impala, Python & Linux Tools



DevOps, Cloud, Big Data, NoSQL, Python & Linux tools. All programs have `--help`.



Hari Sekhon



Cloud & Big Data Contractor, United Kingdom



[![My LinkedIn](https://img.shields.io/badge/LinkedIn%20Profile-HariSekhon-blue?logo=data:image/svg%2bxml;base64,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)](https://www.linkedin.com/in/HariSekhon/)


*(you're welcome to connect with me on LinkedIn)*



**Make sure you run `make update` if updating and not just `git pull` as you will often need the latest library submodule and possibly new upstream libraries**



## Quick Start



### Ready to run Docker image



All programs and their pre-compiled dependencies can be found ready to run on [DockerHub](https://hub.docker.com/r/harisekhon/pytools/).



List all programs:



```shell

docker run harisekhon/pytools

```



Run any given program:



```shell

docker run harisekhon/pytools  

```



### Automated Build from source



installs git, make, pulls the repo and build the dependencies:



```shell

curl -L https://git.io/python-bootstrap | sh

```



or manually:



```shell

git clone https://github.com/HariSekhon/DevOps-Python-tools pytools

cd pytools

make

```



To only install pip dependencies for a single script, you can just type make and the filename with a `.pyc` extension

instead of `.py`:



```shell

make anonymize.pyc

```



Make sure to read [Detailed Build Instructions](https://github.com/HariSekhon/DevOps-Python-tools#detailed-build-instructions) further down for more information.



Some Hadoop tools with require Jython, see [Jython for Hadoop Utils](https://github.com/HariSekhon/DevOps-Python-tools#jython-for-hadoop-utils) for details.



### Usage



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



Environment variables are supported for convenience and also to hide credentials from being exposed in the process list

eg. `$PASSWORD`, `$TRAVIS_TOKEN`. These are indicated in the `--help` descriptions in brackets next to each option and

often have more specific overrides with higher precedence eg. `$AMBARI_HOST`, `$HBASE_HOST` take priority over `$HOST`.



### DevOps Python Tools - Inventory



- Linux:

  - `anonymize.py` - anonymizes your configs / logs from files or stdin (for pasting to Apache Jira tickets or mailing

  - lists)

    - anonymizations include these and more:

      - hostnames / domains / FQDNs

      - email addresses

      - IP + MAC addresses

      - AWS Access Keys, Secret Keys, ARNs, STS tokens

      - Kerberos principals

      - LDAP sensitive fields (eg. CN, DN, OU, UID, sAMAccountName, member, memberOf...)

      - Cisco & Juniper ScreenOS configurations passwords, shared keys and SNMP strings

    - `anonymize_custom.conf` - put regex of your Name/Company/Project/Database/Tables to anonymize to ``

    - placeholder tokens indicate what was stripped out (eg. ``, ``, ``)

    - `--ip-prefix` leaves the last IP octect to aid in cluster debugging to still see differentiated nodes

      communicating with each other to compare configs and log communications

    - `--hash-hostnames` - hashes hostnames to look like Docker temporary container ID hostnames so that vendors support

      teams can differentiate hosts in clusters

    - `anonymize_parallel.sh` - splits files in to multiple parts and runs `anonymize.py` on each part in parallel

      before re-joining back in to a file of the same name with a `.anonymized` suffix. Preserves order of evaluation

      important for anonymization rules, as well as maintaining file content order. On servers this parallelization can

      result in a 30x speed up for large log files

  - `find_duplicate_files.py` - finds duplicate files in one or more directory trees via multiple methods including file

    basename, size, MD5 comparison of same sized files, or bespoke regex capture of partial file basename

  - `find_active_server.py` - finds fastest responding healthy server or active master in high availability deployments,

    useful for scripting against clustered technologies (eg. Elasticsearch, Hadoop, HBase, Cassandra etc).

    Multi-threaded for speed and highly configurable - socket, http, https, ping, url and/or regex content match. See

    further down for more details and sub-programs that simplify usage for many of the most common cluster technologies

  - `welcome.py` - cool spinning welcome message greeting your username and showing last login time and user to put in

    your shell's `.profile` (there is also a perl version in my [DevOps Perl Tools](https://github.com/harisekhon/perl-tools) repo)

- [Amazon Web Services](https://aws.amazon.com/):

  - `aws_users_access_key_age.py` - lists all users access keys, status, date of creation and age in days. Optionally

    filters for active keys and older than N days (for key rotation governance)

  - `aws_users_unused_access_keys.py` - lists users access keys that haven't been used in the last N days or that have

    never been used (these should generally be removed/disabled). Optionally filters for only active keys

  - `aws_users_last_used.py` - lists all users and their days since last use across both passwords and access keys.

    Optionally filters for users not used in the last N days to find old accounts to remove

  - `aws_users_pw_last_used.py` - lists all users and dates since their passwords were last used. Optionally filters for

    users with passwords not used in the last N days

- [Google Cloud Platform](https://cloud.google.com/):

  - [GCF](https://cloud.google.com/functions) - Google Cloud Functions written in Python:

    - [gcp_cloud_function_sql_export/](https://github.com/HariSekhon/DevOps-Python-tools/tree/master/gcp_cloud_function_sql_export) - runs [Cloud SQL](https://cloud.google.com/sql) export backups to

      [GCS](https://cloud.google.com/storage), subscribing to [PubSub](https://cloud.google.com/pubsub) topic that is

      triggered by [Cloud Scheduler](https://cloud.google.com/scheduler)

      - see the [DevOps Bash tools](https://github.com/HariSekhon/DevOps-Bash-tools/) repo for several related GCP SQL to set up service account permissions and

        [Cloud Scheduler](https://cloud.google.com/scheduler) jobs

    - [gcp_cloud_function_ifconfig/](https://github.com/HariSekhon/DevOps-Python-tools/tree/master/gcp_cloud_function_ifconfig) - debug your cloud function public networking by determining its public IP

      address - use this to test your VPC connector public routing, comparison with firewall rules etc.

    - [gcp_cloud_function_proxy/](https://github.com/HariSekhon/DevOps-Python-tools/tree/master/gcp_cloud_function_proxy) - debug your cloud function networking by querying a given URL to check its

      accessibility, returning the HTTP status code and content. Use this to validate access through firewall rules via

      VPC connector routing

  - `gcp_service_account_credential_keys.py` - lists all GCP service account credential keys for a given project with

    their age and expiry details, optionally filtering by non-expiring, already expired, or will expire within N days

- [Docker](https://www.docker.com/):

  - `docker_registry_show_tags.py` / `dockerhub_show_tags.py` / `quay_show_tags.py` - shows tags for docker repos in a

    docker registry or on [DockerHub](https://hub.docker.com/u/harisekhon/) or [Quay.io](https://quay.io/) - Docker CLI doesn't support this yet but it's a very

    useful thing to be able to see live on the command line or use in shell scripts (use `-q`/`--quiet` to return only

    the tags for easy shell scripting). You can use this to pre-download all tags of a docker image before running tests

    across versions in a simple bash for loop, eg. `docker_pull_all_tags.sh`

  - `dockerhub_search.py` - search DockerHub with a configurable number of returned results (older official

    `docker search` was limited to only 25 results), using `--verbose` will also show you how many results were returned

    to the termainal and how many DockerHub has in total (use `-q / --quiet` to return only the image names for easy

    shell scripting). This can be used to download all of my DockerHub images in a simple bash for loop eg.

    `docker_pull_all_images.sh` and can be chained with `dockerhub_show_tags.py` to download all tagged versions for all

    docker images eg. `docker_pull_all_images_all_tags.sh`

  - `dockerfiles_check_git*.py` - check Git tags & branches align with the containing Dockerfile's `ARG *_VERSION`

- [Spark](https://spark.apache.org/) & Data Format Converters:

  - `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

  - `xml_to_json.py` - XML to JSON converter

  - `json_to_xml.py` - JSON to XML converter

  - `json_to_yaml.py` - JSON to YAML converter

  - `json_docs_to_bulk_multiline.py` - converts json files to bulk multi-record one-line-per-json-document format for

    pre-processing and loading to big data systems like [Hadoop](http://hadoop.apache.org/) and

    [MongoDB](https://www.mongodb.com/), can recurse directory trees, and mix json-doc-per-file / bulk-multiline-json /

    directories / standard input, combines all json documents and outputs bulk-one-json-document-per-line to standard

    output for convenient command line chaining and redirection, optionally continues on error, collects broken records

    to standard error for logging and later reprocessing for bulk batch jobs, even supports single quoted json while not

    technically valid json is used by MongoDB and even handles embedded double quotes in 'single quoted json'

  - `yaml_to_json.py` - YAML to JSON converter (because some APIs like GitLab CI Validation API require JSON)

  - see also `validate_*.py` further down for all these formats and more

- [Hadoop](http://hadoop.apache.org/) ecosystem & NoSQL:

  - [Ambari](https://hortonworks.com/apache/ambari/):

    - `ambari_blueprints.py` - Blueprint cluster templating and deployment tool using Ambari API

      - list blueprints

      - fetch all blueprints or a specific blueprint to local json files

      - blueprint an existing cluster

      - create a new cluster using a blueprint

      - sorts and prettifies the resulting JSON template for deterministic config and line-by-line diff necessary for

        proper revision control

      - optionally strips out the excessive and overly specific configs to create generic more reusable templates

      - see the `ambari_blueprints/` directory for a variety of Ambari blueprint templates generated by and deployable

        using this tool

    - `ambari_ams_*.sh` - query the Ambari Metrics Collector API for a given metrics, list all metrics or hosts

    - `ambari_cancel_all_requests.sh` - cancel all ongoing operations using the Ambari API

    - `ambari_trigger_service_checks.py` - trigger service checks using the Ambari API

  - [Hadoop](http://hadoop.apache.org/) HDFS:

    - `hdfs_find_replication_factor_1.py` - finds HDFS files with replication factor 1, optionally resetting them to

      replication factor 3 to avoid missing block alerts during datanode maintenance windows

    - `hdfs_time_block_reads.jy` - 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. Helps find cluster data

      layer bottlenecks such as slow datanodes, faulty hardware or misconfigured top-of-rack switch ports.

    - `hdfs_files_native_checksums.jy` - fetches native HDFS checksums for quicker file comparisons (about 100x faster

      than doing `hdfs dfs -cat | md5sum`)

    - `hdfs_files_stats.jy` - fetches HDFS file stats. Useful to generate a list of all files in a directory tree

      showing block size, replication factor, underfilled blocks and small files

  - [Hive](https://hive.apache.org/) / [Impala](https://impala.apache.org/):

    - `hive_schemas_csv.py` / `impala_schemas_csv.py` - dumps all databases, tables, columns and types out in CSV format

      to standard output



    The following programs can all optionally filter by database / table name regex:



    - `hive_foreach_table.py` / `impala_foreach_table.py` - execute any query or statement against every Hive / Impala

      table

    - `hive_tables_row_counts.py` / `impala_tables_row_counts.py` - outputs tables row counts. Useful for reconciliation

      between cluster migrations

    - `hive_tables_column_counts.py` / `impala_tables_column_counts.py` - outputs tables column counts. Useful for

      finding unusually wide tables

    - `hive_tables_row_column_counts.py` / `impala_tables_row_column_counts.py` - outputs tables row and column counts.

      Useful for finding unusually big tables

    - `hive_tables_row_counts_any_nulls.py` / `impala_tables_row_counts_any_nulls.py` - outputs tables row counts where

      any field is NULL. Useful for reconciliation between cluster migrations or catching data quality problems or

      subtle ETL bugs

    - `hive_tables_null_columns.py` / `impala_tables_null_columns.py` - outputs tables columns containing only NULLs.

      Useful for catching data quality problems or subtle ETL bugs

    - `hive_tables_null_rows.py` / `impala_tables_null_rows.py` - outputs tables row counts where all fields contain

      NULLs. Useful for catching data quality problems or subtle ETL bugs

    - `hive_tables_metadata.py` / `impala_tables_metadata.py` - outputs for each table the matching regex metadata DDL

      property from describe table

    - `hive_tables_locations.py` / `impala_tables_locations.py` - outputs for each table its data location

  - [HBase](https://hbase.apache.org/):

    - `hbase_generate_data.py` - inserts random generated data in to a given [HBase](https://hbase.apache.org/) table,

      with optional skew support with configurable skew percentage. Useful for testing region splitting, balancing, CI

      tests etc. Outputs stats for number of rows written, time taken, rows per sec and volume per sec written.

    - `hbase_show_table_region_ranges.py` - dumps HBase table region ranges information, useful when pre-splitting

      tables

    - `hbase_table_region_row_distribution.py` - calculates the distribution of rows across regions in an HBase table,

      giving per region row counts and % of total rows for the table as well as median and quartile row counts per

      regions

    - `hbase_table_row_key_distribution.py` - calculates the distribution of row keys by configurable prefix length in

      an HBase table, giving per prefix row counts and % of total rows for the table as well as median and quartile row

      counts per prefix

    - `hbase_compact_tables.py` - compacts HBase tables (for off-peak compactions). Defaults to finding and iterating

      on all tables or takes an optional regex and compacts only matching tables.

    - `hbase_flush_tables.py` - flushes HBase tables. Defaults to finding and iterating on all tables or takes an

      optional regex and flushes only matching tables.

    - `hbase_regions_by_*size.py` - queries given RegionServers JMX to lists topN regions by storeFileSize or

      memStoreSize, ascending or descending

    - `hbase_region_requests.py` - calculates requests per second per region across all given RegionServers or average

      since RegionServer startup, configurable intervals and count, can filter to any combination of reads / writes /

      total requests per second. Useful for watching more granular region stats to detect region hotspotting

    - `hbase_regionserver_requests.py` - calculates requests per regionserver second across all given regionservers or

      average since regionserver(s) startup(s), configurable interval and count, can filter to any combination of read,

      write, total, rpcScan, rpcMutate, rpcMulti, rpcGet, blocked per second. Useful for watching more granular

      RegionServer stats to detect RegionServer hotspotting

    - `hbase_regions_least_used.py` - finds topN biggest/smallest regions across given RegionServers than have received

      the least requests (requests below a given threshold)

  - [OpenTSDB](http://opentsdb.net/):

    - `opentsdb_import_metric_distribution.py` - calculates metric distribution in bulk import file(s) to find data skew

      and help avoid HBase region hotspotting

    - `opentsdb_list_metrics*.sh` - lists OpenTSDB metric names, tagk or tagv via OpenTSDB API or directly from HBase

      tables with optionally their created date, sorted ascending

  - [Pig](https://pig.apache.org/)

    - `pig-text-to-elasticsearch.pig` - bulk index unstructured files in [Hadoop](http://hadoop.apache.org/) to

      [Elasticsearch](https://www.elastic.co/products/elasticsearch)

    - `pig-text-to-solr.pig` - bulk index unstructured files in [Hadoop](http://hadoop.apache.org/) to

      [Solr](http://lucene.apache.org/solr/) / [SolrCloud clusters](https://wiki.apache.org/solr/SolrCloud)

    - `pig_udfs.jy` - Pig Jython UDFs for Hadoop

- `find_active_server.py` - returns first available healthy server or active master in high availability deployments,

  useful for chaining with single argument tools. Configurable tests include socket, http, https, ping, url and/or regex

  content match, multi-threaded for speed. Designed to extend tools that only accept a single `--host` option but for

  which the technology has later added multi-master support or active-standby masters (eg. Hadoop, HBase) or where you

  want to query cluster wide information available from any online peer (eg. Elasticsearch)

  - The following are simplified specialisations of the above program, just pass host arguments, all the details have

    been baked in, no switches required

    - `find_active_hadoop_namenode.py` - returns active [Hadoop](http://hadoop.apache.org/) Namenode in HDFS HA

    - `find_active_hadoop_resource_manager.py` - returns active [Hadoop](http://hadoop.apache.org/) Resource Manager in Yarn HA

    - `find_active_hbase_master.py` - returns active [HBase](https://hbase.apache.org/) Master in HBase HA

    - `find_active_hbase_thrift.py` - returns first available [HBase](https://hbase.apache.org/) Thrift Server (run

      multiple of these for load balancing)

    - `find_active_hbase_stargate.py` - returns first available [HBase](https://hbase.apache.org/) Stargate rest server

      (run multiple of these for load balancing)

    - `find_active_apache_drill.py` - returns first available [Apache Drill](https://drill.apache.org/) node

    - `find_active_cassandra.py` - returns first available [Apache Cassandra](https://cassandra.apache.org/) node

    - `find_active_impala*.py` - returns first available [Impala](https://impala.apache.org/) node of either Impalad,

      Catalog or Statestore

    - `find_active_presto_coordinator.py` - returns first available [Presto](https://prestodb.io/) Coordinator

    - `find_active_kubernetes_api.py` - returns first available [Kubernetes](https://kubernetes.io/) API server

    - `find_active_oozie.py` - returns first active [Oozie](http://oozie.apache.org/) server

    - `find_active_solrcloud.py` - returns first available [Solr](http://lucene.apache.org/solr/) / [SolrCloud](https://wiki.apache.org/solr/SolrCloud) node

    - `find_active_elasticsearch.py` - returns first available [Elasticsearch](https://www.elastic.co/products/elasticsearch) node

    - see also: [Advanced HAProxy configurations](https://github.com/HariSekhon/HAProxy-configs) which are part of the

      [Advanced Nagios Plugins Collection](https://github.com/HariSekhon/Nagios-Plugins)

- [Travis CI](https://travis-ci.org/):

  - `travis_last_log.py` - fetches [Travis CI](https://travis-ci.org/) latest running / completed / failed build log for given repo -

    useful for quickly getting the log of the last failed build when CCMenu or BuildNotify applets turn red

  - `travis_debug_session.py` - launches a [Travis CI](https://travis-ci.org/) interactive debug build session via Travis API, tracks

    session creation and drops user straight in to the SSH shell on the remote Travis build, very convenient one shot

    debug launcher for Travis CI

- `selenium_hub_browser_test.py` - checks [Selenium Grid Hub / Selenoid](https://www.selenium.dev/documentation/en/grid/) is working by calling browsers such as

  Chrome and Firefox to fetch a given URL and content/regex match the result

- Data Validation (useful in CI):

  - `validate_*.py` - validate files, directory trees and/or standard input streams

    - supports the following file formats:

      - Avro

      - CSV

      - INI / Java Properties (also detects duplicate sections and duplicate keys within sections)

      - JSON (both normal and json-doc-per-line bulk / big data format as found in MongoDB and Hadoop json data files)

      - LDAP LDIF

      - Parquet

      - XML

      - YAML

    - directories are recursed, testing any files with relevant matching extensions (`.avro`, `.csv`, `json`, `parquet`,

      `.ini`/`.properties`, `.ldif`, `.xml`, `.yml`/`.yaml`)

    - used for Continuous Integration tests of various adjacent Spark data converters as well as configuration files for

      things like Presto, Ambari, Apache Drill etc found in my [DockerHub](https://hub.docker.com/u/harisekhon/) images

      [Dockerfiles master repo](https://github.com/HariSekhon/Dockerfiles) which contains docker builds and configurations for many open source Big Data &

      Linux technologies



### Detailed Build Instructions



#### Python VirtualEnv localized installs



The automated build will use 'sudo' to install required Python PyPI libraries to the system unless running as root or it

detects being inside a VirtualEnv. If you want to install some of the common Python libraries using your OS packages

instead of installing from PyPI then follow the Manual Build section below.



### Manual Setup



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



```shell

git clone https://github.com/HariSekhon/DevOps-Python-tools pytools

cd pytools

git submodule init

git submodule update

sudo pip install -r requirements.txt

```



### Offline Setup



Download the DevOps Python Tools and Pylib git repos as zip files:



Unzip both and move Pylib to the `pylib` folder under DevOps Python Tools.



```shell

unzip devops-python-tools-master.zip

unzip pylib-master.zip



mv -v devops-python-tools-master pytools

mv -v pylib-master pylib

mv -vf pylib pytools/

```



Proceed to install PyPI modules for whichever programs you want to use using your usual procedure - usually an internal

mirror or proxy server to PyPI, or rpms / debs (some libraries are packaged by Linux distributions).



All PyPI modules are listed in the `requirements.txt` and `pylib/requirements.txt` files.



Internal Mirror example ([JFrog Artifactory](https://jfrog.com/artifactory/) or similar):



```shell

sudo pip install --index https://host.domain.com/api/pypi/repo/simple --trusted host.domain.com -r requirements.txt

```



Proxy example:



```shell

sudo pip install --proxy hari:mypassword@proxy-host:8080 -r requirements.txt

```



#### Mac OS X



The automated build also works on Mac OS X but you'll need to install [Apple XCode](https://developer.apple.com/download/) (on recent Macs just typing

`git` is enough to trigger Xcode install).



I also recommend you get [HomeBrew](https://brew.sh/) to install other useful tools and libraries you may need like OpenSSL for

development headers and tools such as wget (these are installed automatically if Homebrew is detected on Mac OS X):



```shell

bash-tools/install/install_homebrew.sh

```



```shell

brew install openssl wget

```



If failing to build an OpenSSL lib dependency, just prefix the build command like so:



```shell

sudo OPENSSL_INCLUDE=/usr/local/opt/openssl/include OPENSSL_LIB=/usr/local/opt/openssl/lib ...

```



You may get errors trying to install to Python library paths even as root on newer versions of Mac, sometimes this is

caused by pip 10 vs pip 9 and downgrading will work around it:



```shell

sudo pip install --upgrade pip==9.0.1

make

sudo pip install --upgrade pip

make

```



### Jython for Hadoop Utils



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



```shell

hdfs_time_block_reads.jy

hdfs_files_native_checksums.jy

hdfs_files_stats.jy

```



Run like so:



```shell

jython -J-cp $(hadoop classpath) hdfs_time_block_reads.jy --help

```



The `-J-cp $(hadoop classpath)` part dynamically inserts the current Hadoop java classpath required to use the Hadoop

APIs.



See below for procedure to install Jython if you don't already have it.



#### Automated Jython Install



This will download and install jython to /opt/jython-2.7.0:



```shell

make jython

```



#### Manual Jython Install



Jython is a simple download and unpack and can be fetched from 



Then add the Jython install bin directory to the $PATH or specify the full path to the `jython` binary, eg:



```shell

/opt/jython-2.7.0/bin/jython hdfs_time_block_reads.jy ...

```



### Configuration for Strict Domain / FQDN validation



Strict validations include host/domain/FQDNs using TLDs which are populated from the official IANA list is done via my

[PyLib](https://github.com/HariSekhon/pylib) library submodule - see there for details on configuring this to permit custom TLDs like `.local`,

`.intranet`, `.vm`, `.cloud` etc. (all already included in there because they're common across companies internal

environments).



### Python SSL certificate verification problems



If you end up with an error like:



```shell

./dockerhub_show_tags.py centos ubuntu

[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:765)

```



It can be caused by an issue with the underlying Python + libraries due to changes in OpenSSL and certificates. One

quick fix is to do the following:



```shell

sudo pip uninstall -y certifi &&

sudo pip install certifi==2015.04.28

```



### Updating



Run:



```shell

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).



### Testing



[Continuous Integration](https://travis-ci.org/HariSekhon/devops-python-tools) is run on this repo with tests for success and failure scenarios:



- unit tests for the custom supporting [python library](https://github.com/HariSekhon/pylib)

- integration tests of the top level programs using the libraries for things like option parsing

- [functional tests](https://github.com/HariSekhon/DevOps-Python-tools/tree/master/tests) for the top level programs using local test data and [Docker containers](https://hub.docker.com/u/harisekhon/)



To trigger all tests run:



```shell

make test

```



which will start with the underlying libraries, then move on to top level integration tests and functional tests using

docker containers if docker is available.



### Contributions



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



You might also be interested in the following really nice Jupyter notebook for HDFS space analysis created by another

Hortonworks guy Jonas Straub:



## Star History



[![Star History Chart](https://api.star-history.com/svg?repos=HariSekhon/DevOps-Python-tools&type=Date)](https://star-history.com/#HariSekhon/DevOps-Python-tools&Date)



[git.io/python-tools](https://git.io/python-tools)



[git.io/pytools](https://git.io/pytools)



## More Core Repos



### Knowledge



[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=Knowledge-Base&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/Knowledge-Base)

[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=Diagrams-as-Code&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/Diagrams-as-Code)



### DevOps Code



[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=DevOps-Bash-tools&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/DevOps-Bash-tools)

[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=DevOps-Python-tools&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/DevOps-Python-tools)

[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=DevOps-Perl-tools&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/DevOps-Perl-tools)

[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=DevOps-Golang-tools&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/DevOps-Golang-tools)



### Containerization



[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=Kubernetes-configs&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/Kubernetes-configs)

[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=Dockerfiles&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/Dockerfiles)



### CI/CD



[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=GitHub-Actions&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/GitHub-Actions)

[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=Jenkins&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/Jenkins)



### DBA - SQL



[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=SQL-scripts&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/SQL-scripts)



### DevOps Reloaded



[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=Nagios-Plugins&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/Nagios-Plugins)

[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=HAProxy-configs&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/HAProxy-configs)

[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=Terraform&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/Terraform)

[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=Packer-templates&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/Packer-templates)

[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=Nagios-Plugin-Kafka&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/Nagios-Plugin-Kafka)



### Templates



[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=Templates&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/Templates)

[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=Template-repo&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/Template-repo)



### Misc



[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=Spotify-tools&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/Spotify-tools)

[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=HariSekhon&repo=Spotify-playlists&theme=ambient_gradient&description_lines_count=3)](https://github.com/HariSekhon/Spotify-playlists)



The rest of my original source repos are

[here](https://github.com/HariSekhon?tab=repositories&q=&type=source&language=&sort=stargazers).



Pre-built Docker images are available on my [DockerHub](https://hub.docker.com/u/harisekhon/).



![](https://hit.yhype.me/github/profile?user_id=2211051)