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Recent Posts
Categories
- Design (2)
- Elasticsearch (3)
- Golang (5)
- hadoop (2)
- Java (12)
- Kubernetes (1)
- linux (1)
- Maven (1)
- Openshift (1)
- Performance (1)
- Scala (2)
- Security (3)
- Spark (2)
- Spring (3)
- Spring Batch (1)
- Spring Boot (2)
- sqoop (1)
- UI (1)
- unix (1)
- Vim (2)
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Tags
annotation
apache hive 3
build
cdp
cloudera
commands
CORS
design
design pattern
DNS
elastic
elasticsearch
go
golang
hadoop
hdfs
hive
http
ip
java
jms
junit
maven
mq
mysql
nginx
oracle
proxy
proxy_pass
queue
rdbms
Repository
resolution
resolver
reverseproxy
scala
spark
spring
spring boot
springboot
sqoop
string
time zone
upstream
vim
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Apache Hive 3 Changes in CDP Upgrade: Part-1
INTRODUCTION Cloudera Data Platform (CDP) is a cloud computing platform for businesses. It provides integrated and multifunctional self-service tools in order to analyze and centralize data. It brings security and governance. read more…
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Generate Sequential and Unique IDs in a Spark Dataframe
Apache Spark is an open source, general-purpose distributed computing engine used for processing and analyzing a large amount of data. Hence, adding sequential and unique IDs to a Spark Dataframe is not very straight forward, because of distributed nature of it.
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Spark Partitions with Coalesce and Repartition (hash, range, round robin)
One main advantage of the Apache Spark is, it splits data into multiple partitions and executes operations on all partitions of data in parallel which allows us to complete the job faster.While working with partition data we often need to increase or decrease the partitions based on data distribution. Methods repartition and coalesce helps us to repartition.
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