Lazy Evaluation & DAG in Spark

Lazy Evaluation

  • Evaluation means executing the logic. Lazy means don't execute immediately until some action is performed. So code will execute when the action is performed.
  • Lazy evaluation means spark will not load or transform data unless an action is performed.
    • Load file into RDD
    • Filter the RDD
    • Count no of elements (Only now loading filtering happens)
  • Helps internally optimize operations and resource usage.
  • We can write chaining operations and We can watch out during the troubleshooting.
  • If you only used transformations like textFile then it will not execute immediately it will create DAG. Those transformations are compiled into DAG. DAG is a complex piece of module. 
  • You can perform bunch of transformations before invoking action. 
  • Until Action is performed the spark compiler checks whether required transformations code is compiled in DAG. 
  • DAG is graph so it will be having multiple paths, it will take any one path and execute the transformations whenever the action is called.

DAG

  • In Apache Spark, a DAG (Directed Acyclic Graph) is a fundamental concept that represents the logical execution plan of a Spark job.
  • The DAG is "Directed" because it shows the flow of data from one transformation to another, and it's "Acyclic" because there are no cycles or loops in the graph. This means that each transformation depends only on its immediate predecessors and not on itself or any transformations further downstream.
  • The DAG is built when you create a Spark DataFrame or RDD and apply transformations to it.
  • Spark's Catalyst optimizer analyzes this DAG to generate an optimized physical execution plan that minimizes data movement and maximizes parallelism.

Comments

Post a Comment

Popular posts from this blog

Hive File Formats

Image
 Hive supports several file formats: Text File SequenceFile RCFile Avro Files ORC Files Parquet Custom INPUTFORMAT and OUTPUTFORMAT      The hive.default.fileformat configuration parameter which is avaialble in hive-site.xml determines the format to use if it is not specified in a CREATE TABLE or ALTER TABLE statement.        Text file is the parameter's default value. What is File Format ? File Format is a way in which information is stored or encoded in a computer file.  In Hive it refers to how records are stored inside the file. These file formats mainly vary between data encoding, compression rate, usage of space and disk I/O. Hive does not verify whether the data that you are loading matches the schema for the table or not. However, it verifies if the file format matches the table definition or not. Text File TEXT FILE format is a famous input/output format used in Hadoop. In Hive if we define a table as TEXTFILE it can load data of from csv (Comma Separated Values), tsv, txt
Read more

HDFS Infographic

Image
Already we know, the data will be getting stored in 3 data nodes (replication) for prevention of failures or corruption. It is useless when you storing the same data in same disk/rack as 3 times. Suppose that whole data replicated single disk/rack is corrupted then it won't be going to work out, It is useless to come and do replication process in HDFS. When we are storing all of these 3 replicated data in different data node but in same rack. It is also not helpful. Suppose if you lose whole rack, then won't be going to help. So we have to store it in different data node with different rack then it will be more helpful to protect the data. Here we are going to discuss about how to store the data in different data node with different rack and how to handle Fault Tolerance. Replica Placement Strategy Name node will follow  Rack Awareness Policy  algorithm for replicating data into 3 different data nodes. This Rack Awareness Policy follows Replica placement strategy. Here the
Read more

Why We Need Hadoop?

What is Hadoop? It is an open source framework or system which was fully developed by Java.  It is allow us to implement Big Data.  Hadoop can be used for storing and processing huge datasets with cluster of commodity hardware. Because its stores the data and processing in a distributed manner.  It made up of several components which entirely open source. Doug Cutting is the inventor of hadoop. He used elephant symbol. His child used to play with elephant toy. So he used that toy name as symbol and named for new invention. Why we need to go for Hadoop? 1. High Throughput and Low latency queries Less amount of time it will take to run/process real time queries or near real time queires. We can achieve high throughput in hadoop using below techniques.  Parallel Processing WORM Data Locality 2. Distributed computing A distributed computer system consists of multiple software components that are on multiple computers, but run as a single system.  The computers that are in a distributed sys
Read more