Because its never late to start taking notes and make it 'public'...

• This repository contains my random (but important) technical notes, which I found useful.
• Please do follow individual sub-folders for more details specific technology.
• Notes so far include topics like Hadoop, Spark, Machine Learning, Java, Python, Unit testing, Clean Code, Py project management,

Follow me on, LinkedIn, Github, Kaggle

get HDFS file size

$ hdfs dfs -du -s -h  hdfs://hadoop-cluster/user/hive/warehouse/hive_schema.db/table
655.2 M  1.9 G  hdfs://hadoop-cluster/user/hive/warehouse/hive_schema.db/table

[size]     [disk space consumed]
655.2 M  * 3 (replication factor)  = 1.9 G
-s : aggregate summary of file lengths
-h : human readable instead long number in bytes 

# Look for specific keywards
hdfs dfs -du -s -h  /user/hive/warehouse/*hive_schema_name*/*hive_table_name* 

# print total size
hdfs dfs -du -s -h  /user/hive/warehouse/*hive_schema_name*/*hive_table_name* | awk '{ total += $1 }; END { print total }'

find and delete HIVE tables matching pattern with beeline

beeline -u $BEELINE_URL --showHeader=false --outputformat=tsv2 -e "show tables from $HIVE_SCHEMA like $PATTERN ;" | xargs -I '{}' beeline -u $BEELINE_URL --showHeader=false --outputformat=tsv2 -e " drop table $HIVE_SCHEMA.{} ;" 

xargs - reads data from standard input (stdin) and executes the command (supplied to it as argument) one or more times based on the input read. Any blanks and spaces in input are treated as delimiters, while blank lines are ignored. 

HIVE: select column names based on reguler expression

https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Select#LanguageManualSelect-REGEXColumnSpecification

set hive.support.quoted.identifiers=none;
select `<reguler expression>` from  hive_table ;

hdfs dfs -checksum

https://community.hortonworks.com/questions/19239/hadoop-checksum-calculation-doubts.html

hdfs dfs -checksum <hdfs url>
<hdfs url>        MD5-of-0MD5-of-512CRC32C        00000200000000000000000024c3cf9f64d08eaafeb25bb9776f793c




- Datanodes are responsible for verifying the data they receive before storing the data and its checksum
- When clients read data from datanodes, they verify checksums as well, comparing them with the ones stored at the datanodes
- 'get' command : HDFS computes a checksum for each block of each file. The checksums for a file are stored separately in a hidden file. When a file is read from HDFS, the checksums in that hidden file are used to verify the file’s integrity. For the get command, the -crc option will copy that hidden checksum file. The -ignorecrc option will skip the checksum checking when copying
- A separate checksum is created for every dfs.bytes-perchecksum bytes of data. The default is 512 bytes3
- Each datanode keeps a persistent log of checksum verifications, so it knows the last time each of its blocks was verified.
- "MD5-of-0MD5-of-512CRC32C" : http://mail-archives.apache.org/mod_mbox/hadoop-hdfs-user/201508.mbox/%3CCAMm20=5K+f3ArVtoo9qMSesjgd_opdcvnGiDTkd3jpn7SHkysg@mail.gmail.com%3E

installing jupyter on windows

# On command prompt
C:\> python -m pip install jupyter

# It creats entry in C:\Python36\Scripts
C:\Python36\Scripts\jupyter.exe

C:\>jupyter notebook
[I 12:36:39.808 NotebookApp] Serving notebooks from local directory: C:\
[I 12:36:39.808 NotebookApp] 0 active kernels
[I 12:36:39.808 NotebookApp] The Jupyter Notebook is running at:
[I 12:36:39.808 NotebookApp] http://localhost:8888/?token=1d3293c485f32b492a93cf6dae1088d51d6d4635dff7630d
[I 12:36:39.808 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 12:36:39.808 NotebookApp]

    Copy/paste this URL into your browser when you connect for the first time,
    to login with a token:
        http://localhost:8888/?token=1d3293c485f32b492a93cf6dae1088d51d6d4635dff7630d
[I 12:36:39.976 NotebookApp] Accepting one-time-token-authenticated connection from ::1

Test your Knowledge with Stack Overflow

https://medium.com/dunder-data/how-to-learn-pandas-108905ab4955 You don’t really know a Python library if you cannot answer the majority of questions on it that are asked on Stack Overflow. This statement might be a little too strong, but in general, Stack Overflow provides a great testing ground for your knowledge of a particular library. There are over 50,000 questions tagged as pandas, so you have an endless test bank to build your pandas knowledge.

If you have never answered a question on Stack Overflow, I would recommend looking at older questions that already have answers and attempting to answer them by only using the documentation. After you feel like you can put together high-quality answers, I would suggest making attempts at unanswered questions. Nothing improved my pandas skills more than answering questions on Stack Overflow.

Alter HIVE table name

https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-Create/Drop/Alter/UseDatabase
This statement lets you change the name of a table to a different name. As of version 0.6, a rename on a managed table moves its HDFS location. Rename has been changed as of version 2.2.0 (HIVE-14909) so that a managed table's HDFS location is moved only if the table is created without a LOCATION clause and under its database directory. Hive versions prior to 0.6 just renamed the table in the metastore without moving the HDFS location.

ALTER TABLE old_table RENAME TO new_table;

Rename table records not visible in pyspark! There is a property of table which pyspark api looks for lading data. hive rename command fails to update this property, and we see no records when query from pyspark. Resolution is to alter table to change 'serdeproperties' path.

alter table my_schema.new_table set serdeproperties ('path'='hdfs://hadoop-cluster/user/hive/warehouse/my_schema.db/new_table') 

The GIT stuff

commonly used commands in the order that I follow mostly :-)

https://www.youtube.com/watch?v=47uih9Tp6H8
https://www.atlassian.com/git/tutorials/comparing-workflows/gitflow-workflow

git clone <git dir path>
git init
git pull
git checkout -b feature/XYZ # change branch to feature branch
git add . # . is for add all the files that changed
git status
git commit -m "comments"
git push origin <branch name> 

git rm file1.txt
#But if you want to remove the file only from the Git repository and not remove it from the filesystem, use:
git rm --cached ipmvp-nifi-custom.iml

• If git pull want to specifiy exact remote branch
  Ex: There is no tracking information for the current branch. Please specify which branch you want to merge with.

    git branch --set-upstream-to=origin/<branch> feature/XXX-100

• Create new branch and push to git

git branch feature/ABC-123
git checkout feature/ABC-123
git add .
git commit -m "bla bla"
git push origin feature/ABC-123

• Clear all local commits!

git reset --hard HEAD^

git checkout develop
git pull
git checkout - # switch to feature branch
git reset --hard HEAD # only if you have to cleanup locall changes
git rebase develop

• git submodule

https://blog.github.com/2016-02-01-working-with-submodules/
https://git-scm.com/book/de/v1/Git-Tools-Submodule

• git release with git-flow extensions

  • release/merge feature branch to develop and master

  git flow release start 0.1.0
  git checkout master
  git checkout merge release/0.1.0
  git flow release finish '0.1.0'
  

  • 'tags' - once master updated with new changes, it should be tagged with the updated version number.

• git setup the connection or 407 error

https://stackoverflow.com/questions/24907140/git-returns-http-error-407-from-proxy-after-connect

git config --global http.proxy http://username:password@proxiURL:proxiPort

git config --global http.sslVerify false

• Git Rebase

https://medium.com/@fredrikmorken/why-you-should-stop-using-git-rebase-5552bee4fed1
https://www.jetbrains.com/help/pycharm/apply-changes-from-one-branch-to-another.html Rebase the develop branch with feature. I.e. pull the updates from develop to feature branch.

# on the feature branch
  git checkout develop
  git pull origin develop # to update local develop
  git pull --rebase origin develop 
  git rebase --continue # to confirm if rebase cpompleted
  git status

git flow

https://danielkummer.github.io/git-flow-cheatsheet/

• merge develop to master by creating release branch.
• version of new release is same as release branch name.

steps using git flow:

• git flow for feature branch

git flow init  
git flow feature start MYFEATURE
git flow feature finish MYFEATURE
git flow feature publish MYFEATURE
git flow feature pull origin MYFEATURE
git flow feature track MYFEATURE

• git flow for release

git flow release start RELEASE  
git flow release publish RELEASE
git flow release finish RELEASE
git push origin --tags

git merge conflicts

https://www.atlassian.com/git/tutorials/using-branches/merge-conflicts

• When feature branch needs to merge with develop that is already updated with some other changes

Steps:

1. first update feature from develop.
2. merge conflicts
3. then merge feature to develop
4. merge conflicts

AWS CLI

Install CLI : https://docs.aws.amazon.com/cli/latest/userguide/awscli-install-windows.html#awscli-install-windows-path

Create a profile

C:\Users\vkbomb>aws configure --profile test
AWS Access Key ID []: XXX
AWS Secret Access Key []: YY
Default region name []: eu-central-1
Default output format [None]:

List S3 objects

C:\Users>aws s3 ls s3://bucket_name/folder1/ --profile test

Download data from buckte

C:\Users>aws s3 cp s3://bucket_name/folder1/test_file.txt --profile test .
download: s3://bucket_name/folder1/test_file.txt to .\test_file.txt

#to download entire bucket  
C:\Users>aws s3 sync s3://bucket_name/folder1 --profile test .

Python Decorator and Java annotation

http://supercoderz.in/how-python-decorators-are-better-than-java-annotations/ https://stackoverflow.com/questions/15347136/is-a-python-decorator-the-same-as-java-annotation-or-java-with-aspects

The Gradle Wrapper

https://docs.gradle.org/current/userguide/gradle_wrapper.html

The recommended way to execute any Gradle build is with the help of the Gradle Wrapper (in short just “Wrapper”). The Wrapper is a script that invokes a declared version of Gradle, downloading it beforehand if necessary. As a result, developers can get up and running with a Gradle project quickly without having to follow manual installation processes saving your company time and money.

.
├── build.gradle
├── settings.gradle
├── gradle
│   └── wrapper
│       ├── gradle-wrapper.jar
│       └── gradle-wrapper.properties
├── gradlew
└── gradlew.bat

- build.gradle
mainClassName = 'com.myEmp.nifi.Main'
// this tells the build to run the code from class Main
// so when we build the project, it actually call the code .  

remove zero byte files from HDFS

#List the zero byte files
hdfs dfs -ls hdfs://<path> | awk '{ if ($5 == 0) print $8 }'

#delete all zero byte files
hdfs dfs -ls hdfs://<path> | awk '{ if ($5 == 0) print $8 }' | xargs hadoop fs -rm

# delete zero byte files from subfolders 
hdfs dfs -lsr hdfs://<path>/\* | awk '{ if ($5 == 0) print $8 }' | xargs hadoop fs -rm

#\* avoides the folder itself to comming in the list

Commands to generate a keytab file, using the Kerberos password

$ vim test123.sh
rm -f <user>.keytab  
echo -e " add_entry -password -p <user>@<server> -k 5 -e aes256-cts-hmac-sha1-96\n<pwd>\nwkt <user>.keytab" |ktutil
echo -e " add_entry -password -p <user>@<server> -k 5 -e aes128-cts-hmac-sha1-96\n<pwd>\nwkt <user>.keytab" |ktutil
echo -e " add_entry -password -p <user>@<server> -k 5 -e des3-cbc-sha1\n<pwd>\nwkt <user>.keytab" |ktutil
echo -e " add_entry -password -p <user>@<server> -k 5 -e arcfour-hmac\n<pwd>\nwkt <user>.keytab" |ktutil

OR

$ ktutil
ktutil:  add_entry -password -p <user>@<server> -k 5 -e aes256-cts-hmac-sha1-96
ktutil:  add_entry -password -p <user>@<server> -k 5 -e aes128-cts-hmac-sha1-96
ktutil:  add_entry -password -p <user>@<server> -k 5 -e des3-cbc-sha1
ktutil:  add_entry -password -p <user>@<server> -k 5 -e arcfour-hmac
ktutil:  wkt ./<user>.keytab
ktutil:  quit

HIVE create table if not exists & create or replace view

• 'create or replace view' dose not supported very well, hence it is wise to drop view before hand.
• and use CREATE VIEW [IF NOT EXISTS]

What's in the HIVE JDBC URL

https://streever.atlassian.net/wiki/spaces/HADOOP/pages/9961474/Hive+JDBC+Extended+Connection+URL+Examples https://cwiki.apache.org/confluence/display/Hive/HiveServer2+Clients

jdbc:hive2://zookeeper_quorum|hs2_host:port
/[db][;principal=<hs2_principal>/<hs2_host>|_HOST@<KDC_REALM>]
[;transportMode=binary|http]
[;httpPath=<http_path>]
[;serviceDiscoveryMode=zookeeper;zooKeeperNamespace=<zk_namespace>]

• Adding ;SocketTimeout=0 in URL could solve the problems like below

ERROR processing query/stat
ement. Error Code: 0, SQL state: TStatus(statusCode:ERROR_STATUS, infoMessages:[*org.apache.hive.service.cli.HiveSQLException:Invalid S
essionHandle: SessionHandle [xxx]:12:11,

HIVE nonequality conditions in the join

Nonequality condition (like <>) dose not work with join condition, so below will through error!

select *
from table_a a join table_b b
on (a.field1 <> b.field2)

Use these conditions in where instead

select *
from table_a a join table_b b
where a.field1 <> b.field2

HIVE create external table using schema from another table

create external table tbl_ext 
like tbl_abc
location '/user/hive/xyz...'

HADOOP / HIVE uncompress .zip files

http://gethue.com/extract-archives-as-oozie-job/
https://stackoverflow.com/questions/34573279/how-to-unzip-gz-files-in-a-new-directory-in-hadoop

understanding the log4j

https://logging.apache.org/log4j/1.2/manual.html
https://docs.oracle.com/cd/E29578_01/webhelp/cas_webcrawler/src/cwcg_config_log4j_file.html

• The log4j.properties file sets the logging properties.

• Log4j has three main components: loggers, appenders and layouts

• Logging to the console

log4j.rootLogger=ERROR,stdout
log4j.logger.com.endeca=INFO
# Logger for crawl metrics
log4j.logger.com.endeca.itl.web.metrics=INFO

log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%p\t%d{ISO8601}\t%r\t%c\t[%t]\t%m%n

• Logging to a file

# initialize root logger with level ERROR for stdout and fout
log4j.rootLogger=ERROR,stdout,fout
# set the log level for these components
log4j.logger.com.endeca=INFO
log4j.logger.com.endeca.itl.web.metrics=INFO

# add a ConsoleAppender to the logger stdout to write to the console
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
# use a simple message format
log4j.appender.stdout.layout.ConversionPattern=%m%n

# add a FileAppender to the logger fout
log4j.appender.fout=org.apache.log4j.FileAppender
# create a log file
log4j.appender.fout.File=crawl.log
log4j.appender.fout.layout=org.apache.log4j.PatternLayout
# use a more detailed message pattern
log4j.appender.fout.layout.ConversionPattern=%p\t%d{ISO8601}\t%r\t%c\t[%t]\t%m%n

systemctl commands

systemctl run xxx
systemctl restart xxx

Data Architect

https://blog.treasuredata.com/blog/2016/03/15/self-study-list-for-data-engineers-and-aspiring-data-architects/

Spark : file splits while saving dataFrame - .coalesce() or .repartition()

https://stackoverflow.com/questions/48143159/spark-write-to-disk-with-n-files-less-than-n-partitions
http://www.gatorsmile.io/anticipated-feature-in-spark-2-2-max-records-written-per-file/

• The number of files that get written out is controlled by the parallelization of your DataFrame or RDD.
• In other words number of files written out is equal to number of task performed on dataframe or partitions.
• So if your data is split across 10 Spark partitions you cannot write fewer than 10 files without reducing partitioning (e.g. coalesce or repartition).

Below code will creat one file, in the user home hdfs folder while saving.

sdf.coalesce(1).write.parquet("sampleFolder_1")
# OR
sdf.repartition(1).write.save("sampleFolder_2")

Bamboo - Continuous Integration

• Bamboo is used to automate the release management for a software application, creating a continuous delivery pipeline.
• Continuous Integration is a software development methodology in which a build, unit tests and integration tests are performed, or triggered, whenever code is committed to the repository, to ensure that new changes integrate well into the existing code base.
• Plan
  • high level flow defined.
  • Create new plan each time to create new deployment flow.
  • need to mention a. project name, b. paln name and c. connection to code repository , while creating plan
• Stage
  • each plan have multiple stages
• Jobs
• Task
  • jobs can have multiple tasks
  • Task do real work like execute script, call a function.
• Bamboo could be setup to trigger build when code commited to repo

Data quality issues

1. imcompleted - missing data
2. inaccuracy - garbage chars / wrong data
3. inconsistency - price in diff currency
4. invalidity - logically / business logically invalide data
5. Non stadard - decimal places not consistance

• Data profiling - data quality metrics : %complete, %accurate,
  • threshold to validate in meta or control table
• Data cleansing - remove duplicate, null / NA

ZIP and UNZIP

• Exclude specific folders and files

zip -9r algo.zip . -x "notebook/*"  "tests/*" ".git/*"

• Only list but not unzip

unzip -vl algo.zip

Linux Screen Windows

Keep running your script though you disconnect from session.

• https://linuxize.com/post/how-to-use-linux-screen/

• Start new screen window

screen -S session_name

• Detach from Linux Screen Session

Ctrl+a d

• Return back to screen

screen -r session_name

screen -ls