everyone
I am new to Hadoop World and i have some problem with Hbase join.
I have two cluster,clusterA's Hbase have employee table ,clusterB's Hbase have department table.
So,how to join empolyee and department ?
Should i need to install Hive ?
If the tables are in two separate clusters, you'll need to get one of the HBase tables from one cluster to another. This can be done via sqoop.
From there, you could, in theory, use Phoenix as suggested by Vignesh I in the comments, however, there are some limitations there. You would need to create a Phoenix view of both of those HBase tables. Native HBase views in Phoenix, currently, do not automatically update if they are updated outside of Phoenix, which most native HBase tables would be. This effectively renders views of native HBase tables in Phoenix snapshots instead of views; you will need to rebuild any indexes on a regular basis (and potentially stats as well) in order to capture any updates to the underlying HBase tables.
There is a JIRA open to enhance this behavior so that it would auto update, but the ETA of such a feature is unknown at this time.
What I would recommend, unless you have very specific real-time needs (in which case Phoenix, if you could live with the view limitations, may be the better choice), is to use Pig.
Within the Pig script, you can join the two HBase tables and then perform various transformations.
Hive would be another option, but in that case, you would need to sqoop both tables from HBase into Hive, and then proceed from there within Hive.
Related
I have started learning Hadoop.I understood that HDFS provides distributed storage system and Mapreduce is for data processing.Now i ma reading Hadoop ecosystem.
From the definition of Hive, it is a data ware house built on hadoop for providing SQL like interface.
My question is when hadoop provides HDFS which is falut tolerant , distributed then why hive? Does hive replaces HDFS?.
Does hive provide only sql interface or storage also?
Hive does not replace HDFS. Hive provides sql type interface to data that is stored in HDFS. Its basically used for querying and analysis of data that is stored. Hive in a sense actually eliminates a lot of boiler plate code, that you would have to write if you were using mapreduce. for example just think of how you are going to create different types of joins(left, right, bucketed) or group by clause or any other sql clause in mapreduce and you will get your answer (you lines of code will easily scale to 100's ). Hive provides them out-of-the-box. You dont need to write those lengthy programs in mapreduce. Hive already does that for you.
One thing to note is, Hive itself uses Mapreduce behind the scenes. So any group by, count, join is converted to mapreduce jobs only. You can change this though to Tez/Spark.
for your second question, hive does not provide any storage, it just uses a database (derby as default, MySQL would be a good choice if you want to use a different db) as a metastore just to store the metadata related to the tables, partitions, views, buckets etc.. (metadata is like location of tables, type of data stored in tables, partitions info of the tables, created date, modified date etc..) you create with hive.
To answer your question in comment...
Hive can process structured (csv,txt etc) data & semi-structured(xml,json,parquet etc). It cannot process unstructured data like audio, video etc.
Note: Semi structured data can be handled in DDLs and also through spark to be put into Hive.
I encourage you to learn what is external and managed tables in hive too.
Happy learning.
What is the best ways to parallel ingest data from Teradata database into Hadoop with parallel data moving?
If we create a job which is simple opens one session to Teradata database it will take a lot of time to load huge table.
if we create a set of sessions to load data in parallel, and also make Select in each of the sessions, than it will make a set of Full table scans Teradata to produce a data
What is the recommended best practice to load data in parallelised streams and make unnecessary workload to Teradata?
If Tera data supports table partitioning like oracle, you could try reading the table based on partitioning points which will enable parallelism in read...
Other option you have is, split the table into multiple partitions like adding a where clause on indexed column. This will ensure index scan and you can avoid full table scan.
The most scalable way to ingest data into Hadoop form teradata, which i found is to use Teradata connector for hadoop. It is included in Cloudera & Hortonworks distributions. I will show example base on Cloudera documentation, but the same works with Hortonworks as well:
Informatica big Data edition is using standard Scoop invocation via command line and submitting set of parameters to it. So the main question is - which driver to use to make parallel connections between two MPP systems.
Here is the link to the Cloudera documentation:
Using the Cloudera Connector Powered by Teradata
And here is the digest from this documentation (You could find that this connector support different kinds of load balancing between connections):
Cloudera Connector Powered by Teradata supports the following methods for importing data from Teradata to Hadoop:
split.by.amp
split.by.value
split.by.partition
split.by.hash
split.by.amp Method
This optimal method retrieves data from Teradata. The connector creates one mapper per available Teradata AMP, and each mapper subsequently retrieves data from each AMP. As a result, no staging table is required. This method requires Teradata 14.10 or higher.
If you use partition names in the select clause, Power Center will select only the rows within that partition so there won't be duplicate read (don't forget to choose Database partitioning in Informatica session level). However if you use key range partition you have to choose the range as you mentioned in settings. Usually we use NTILE oracle analytical function to split the table into multiple portions so that the read will be unique across the selects. Please let me know if you have any question. If you have range/auto generated/surrogate key column in the table use it in where clause - write a sub-query to divide the table into multiple portions.
From my understanding, Hbase is the Hadoop database and Hive is the data warehouse.
Hive allows to create tables and store data in it, you can also map your existing HBase tables to Hive and operate on them.
why we should use hbase if hive do all that? can we use hive by itself?
I'm confused :(
So in simple terms, with hive you can fire SQL like queries (with some exceptions) on your table/s and is used in batch operation. While with hbase, you can do real time querying and is based on key value pair.
"why we should use hbase if hive do all that? can we use hive by itself" Because Hive doesn't supports updating your data set. So if you have large analytical processing application use Hive and if you have real time get/set/update request processing, use Hbase.
Im trying to get a clear understanding on HBASE.
Hive:- It just create a Tabular Structure for the Underlying Files in
HDFS. So that we can enable the user to have Querying Abilities on the
HDFS file. Correct me if im wrong here?
Hbase- Again, we have create a Similar table Structure, But bit more
in Structured way( Column Oriented) again over HDFS File system.
aren't they both Same considering the type of job they does. except that Hive runs on Mapredeuce.
Also is that true that we cant create a Hbase table over an Already existing HDFS file?
Hive shares a very similar structures to traditional RDBMS (But Not all), HQL syntax is almost similar to SQL which is good for Database Programmer from learning perspective where as HBase is completely diffrent in the sense that it can be queried only on the basis of its Row Key.
If you want to design a table in RDBMS, you will be following a structured approach in defining columns concentrating more on attributes, while in Hbase the complete design is concentrated around the data, So depending on the type of query to be used we can design a table in Hbase also the columns will be dynamic and will be changing at Runtime (core feature of NoSQL)
You said aren't they both Same considering the type of job they does. except that Hive runs on Mapredeuce .This is not a simple thinking.Because when a hive query is executed, a mapreduce job will be created and triggered.Depending upon data size and complexity it may consume time, since for each mapreduce job, there are some number of steps to do by JobTracker, initializing tasks like maps,combine,shufflesort, reduce etc.
But in case we access HBase, it directly lookup the data they indexed based on specified Scan or Get parameters. Means it just act as a database.
Hive and HBase are completely different things
Hive is a way to create map/reduce jobs for data that resides on HDFS (can be files or HBase)
HBase is an OLTP oriented key-value store that resides on HDFS and can be used in Map/Reduce jobs
In order for Hive to work it holds metadata that maps the HDFS data into tabular data (since SQL works on tables).
I guess it is also important to note that in recent versions Hive is evolving to go beyond a SQL way to write map/reduce jobs and with what HortonWorks calls the "stinger initiative" they have added a dedicated file format (Orc) and import Hive's performance (e.g. with the upcoming Tez execution engine) to deliver SQL on Hadoop (i.e. relatively fast way to run analytics queries for data stored on Hadoop)
Hive:
It's just create a Tabular Structure for the Underlying Files in HDFS. So that we can enable the user to have SQL-like Querying Abilities on existing HDFS files - with typical latency up to minutes.
However, for best performance it's recommended to ETL data into Hive's ORC format.
HBase:
Unlike Hive, HBase is NOT about running SQL queries over existing data in HDFS.
HBase is a strictly-consistent, distributed, low-latency KEY-VALUE STORE.
From The HBase Definitive Guide:
The canonical use case of Bigtable and HBase is the webtable, that is, the web pages
stored while crawling the Internet.
The row key is the reversed URL of the page—for example, org.hbase.www. There is a
column family storing the actual HTML code, the contents family, as well as others
like anchor, which is used to store outgoing links, another one to store inbound links,
and yet another for metadata like language.
Using multiple versions for the contents family allows you to store a few older copies
of the HTML, and is helpful when you want to analyze how often a page changes, for
example. The timestamps used are the actual times when they were fetched from the
crawled website.
The fact that HBase uses HDFS is just an implementation detail: it allows to run HBase on an existing Hadoop cluster, it guarantees redundant storage of data; but it is not a feature in any other sense.
Also is that true that we cant create a Hbase table over an already
existing HDFS file?
No, it's NOT true. Internally HBase stores data in its HFile format.
We are exploring options to archive data in warehouse or RDMS to Hadoop.
As matter of fact I have to use sqoop to load data in to HDFS and probably have to compress it. Then delete the rows which are to be archived.
Trouble is when I have foreign key relation between two tables. I need to maintain data consistency between tables. Please help me with approach.
Luckyly I could find a solution for this using Sqoop API. I was triggering a join query to select the data from child table first then from parent tables. I had writen all the logic in a java program using Sqoop API.