What is the actual difference, and when should be use the other when data needs to be stored?
Please read this post for a good explanation. But in general, HBASE runs on top of HDFS. HDFS is a distributed file system just like any other file system (Unix/Windows) and HBASE is like a database which reads and writes from that file system just like any other database (MySQL, MSSQL).
Related
I have read that it is recommended to save files more than 10MB in HDFS and store path of that file in HBase. Is there any recommended approach of doing this. Is there any specific configurations or tools like Apache Phoenix that can help us achieve this?
Or all of the saving data in HDFS and then saving the location in HBase then reading the path from HBase then reading data from HDFS with the location all be done manually from the client?
I have a terabyte of data files on different machines i want to collect it on centralized machine for some processing is it advisable to use flume ?
Same amount of data is there in RDBMS which i would like to put in hdfs is it advisable to use sqoop to trasffer terabyte of data? if not what will be alternative
Using Sqoop to transfer few terabytes from RDBMS to HDFS is a great idea, highly recommended. This is Sqoop's intended use case and it does do reliably.
Flume is mostly intended for streaming data, so if the files all have events, and you get new files frequently, then Flume with Spooling Directory source can work.
Otherwise, "HDFS -put" is a good way to copy files to HDFS.
I am looking into using Hive on our Hadoop cluster to then use Presto to do some analytics on the data stored in Hadoop but I am still confused about some things:
Files are stored in Hadoop (some kind of file manager)
Hive needs tables to store data from Hadoop (data manager)
Do both Hadoop and Hive store their data separate or does Hive just use the files from Hadoop? (in terms of hard disk space and so on?)
-> So does Hive import data from Hadoop in tables and leave Hadoop alone or how must I see this?
Can Presto be used without Hive and just on Hadoop directly?
Thanks in advance for answering my questions :)
First things first: files are stored in Hadoop Distributed File System (HDFS). Is that what you call Data manager?
Actually Hive can use both - "regular" files in HDFS or tables which are once again "regular" files with additional metadata stored in special datastore (it is called warehouse).
Concerning Presto - it has a built-in support for Hive metastore, but you can also write your own connector plugin for any data source.
Please read more info about Hive connector configuration here and about connector plugins here.
Is there any way to expose cassandra data as HDFS and then perfom shark/Hive query on HDFS ??
If yes, kindly provide some links to transform cassandra db into HDFS.
You can write identity MapReduce Code which take input from CFS (cassandra filesystem) and dump data to HDFS.
Once you have data in HDFS , you can map a hive table and run queries.
The typical way to access Cassandra data in Hive is to use the CqlStorageHandler.
Details see Hive Support for Cassandra CQL3.
But if you have some reasons to access the data directly, take a look at Cassowary. It is a "Hive storage handler for Cassandra and Shark that reads the SSTables directly. This allows total control over the resources used to run ad-hoc queries so that the impact on real-time Cassandra performance is controlled."
I think you are trying to run Hive/Shark against data already in Cassandra. If that is the case then you don't need to access it as HDFS but you need a hive-handler for using it against Cassandra.
For this you can use Tuplejump's project, CASH The Readme provides the instruction on how to build and use it. If you want to put your "big files" in Cassandra and query on them, like you do from HDFS, you will need a FileSystem that runs on Cassandra like DataStax's CFS present in DSE, or Tuplejump's SnackFS (present in the Calliope Project Early Access Repo)
Disclaimer: I work for Tuplejump, Inc.
You can use Tuplejump Calliope Project.
https://github.com/tuplejump/calliope
Configure external Cassandra Table in Shark(like Hive) using Storage Handler provided in TumpleJump code.
All the best!
Three cassandra hive storage
https://github.com/2013Commons/hive-cassandra for 2.0 and hadoop 2
https://github.com/dvasilen/Hive-Cassandra/tree/HIVE-0.11.0-HADOOP-2.0.0-CASSANDRA-1.2.9
https://github.com/richardalow/cassowary directly from sstable
Hadoop writes the intermediate results to the local disk and the results of the reducer to the HDFS. what does HDFS mean. What does it physically translate to
HDFS is the Hadoop Distributed File System. Physically, it is a program running on each node of the cluster that provides a file system interface very similar to that of a local file system. However, data written to HDFS is not just stored on the local disk but rather is distributed on disks across the cluster. Data stored in HDFS is typically also replicated, so the same block of data may appear on multiple nodes in the cluster. This provides reliable access so that one node's crashing or being busy will not prevent someone from being able to read any particular block of data from HDFS.
Check out http://en.wikipedia.org/wiki/Hadoop_Distributed_File_System#Hadoop_Distributed_File_System for more information.
As Chase indicated, HDFS is Hadoop Distributed File System.
If I may, I recommend this tutorial and video of how HDFS and the Map/Reduce framework works and will serve you as a guide into the world of Hadoop: http://www.cloudera.com/resource/introduction-to-apache-mapreduce-and-hdfs/