- 最新
- 最多得票
- 最多評論
HDFS is a distributed, scalable, and portable file system for Hadoop. An advantage of HDFS is data awareness between the Hadoop cluster nodes managing the clusters and the Hadoop cluster nodes managing the individual steps. For more information, see Hadoop documentation.
HDFS is used by the master and core nodes. One advantage is that it's fast; a disadvantage is that it's ephemeral storage which is reclaimed when the cluster ends. It's best used for caching the results produced by the immediate job flow steps. https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-file-systems.html
Just to provide bit more context. When we say Hadoop or Bigdata its a framework that allows for the distributed processing of large data sets . It primarily comprise of a Distributed storage layer (HDFS) and a compute/processing layer (mapreduce, Hive, Spark etc..). Hence most of the framework (Hive, tez, Hbase, Spark etc..) build around Hadoop/Bigdata framework are designed to read/write/process data from a distributed storage layer . In the early days it was primarily HDFS however over the period of time all these framework started supporting (and providing better integrations) with other distributed storage systems (mainly on cloud storage like S3).
So to answer your query yes HDFS provides the distributed Storage layer, and provides slightly faster data access due to local storage and less network transfer compared external storage system. It was mainly preferable when using long running hadoop cluster. However when using transient Hadoop/EMR cluster HDFS storage layer is lost when cluster is terminated. Hence it is recommended to use S3 as persistent storage layer .
and yes Spark is the compute engine , that supports a wide range of applications, including ETL, machine learning, stream processing, and graph .
相關內容
- 已提問 10 個月前
AWS 官方已更新 2 年前- AWS 官方已更新 2 年前
- AWS 官方已更新 1 年前
@lowflyinghawk, for a AWS EMR cluster (HDFS + Spark), as it's ephemeral storage which is reclaimed when the cluster ends, is it a good idea to save result to s3 after processing data with spark ?
@posix, yes. btw, nice username.
@lowflyinghawk, thank you. Just to motivate me and to remember me to stay positif and determined man. Listen, if I understand well, HDFS is for its capacity to provide high performance for accessing data; and spark to allow to make distributed calcul ?