Is there any reason not use Hive UDF's in Pig 0.15?
I'm thinking mostly about performance, but if there are any other reasons I'd be happy to hear them.
For example, we have a simple Java implementation of lpad that we use. Should we bother keeping it, or can we use the Hive version?
Hive UDFs are supported in pig 0.15 version. See below.
http://hortonworks.com/blog/announcing-apache-pig-0-15-0/
Related
SparkSQL CLI internally uses HiveQL and in case Hive on spark(HIVE-7292) , hive uses spark as backend engine. Can somebody throw some more light, how exactly these two scenarios are different and pros and cons of both approaches?
When SparkSQL uses hive
SparkSQL can use HiveMetastore to get the metadata of the data stored in HDFS. This metadata enables SparkSQL to do better optimization of the queries that it executes. Here Spark is the query processor.
When Hive uses Spark See the JIRA entry: HIVE-7292
Here the the data is accessed via spark. And Hive is the Query processor. So we have all the deign features of Spark Core to take advantage of. But this is a Major Improvement for Hive and is still "in progress" as of Feb 2 2016.
There is a third option to process data with SparkSQL
Use SparkSQL without using Hive. Here SparkSQL does not have access to the metadata from the Hive Metastore. And the queries run slower. I have done some performance tests comparing options 1 and 3. The results are here.
SparkSQL vs Spark API you can simply imagine you are in RDBMS world:
SparkSQL is pure SQL, and Spark API is language for writing stored procedure
Hive on Spark is similar to SparkSQL, it is a pure SQL interface that use spark as execution engine, SparkSQL uses Hive's syntax, so as a language, i would say they are almost the same.
but Hive on Spark has a much better support for hive features, especially hiveserver2 and security features, hive features in SparkSQL is really buggy, there is a hiveserver2 impl in SparkSQL, but in latest release version (1.6.x), hiveserver2 in SparkSQL doesn't work with hivevar and hiveconf argument anymore, and the username for login via jdbc doesn't work either...
see https://issues.apache.org/jira/browse/SPARK-13983
i believe hive support in spark project is really very low priority stuff...
sadly Hive on spark integration is not that easy, there are a lot of dependency conflicts... such as
https://issues.apache.org/jira/browse/HIVE-13301
and, when i'm trying hive with spark integration, for debug purpose, i'm always starting hive cli like this:
export HADOOP_USER_CLASSPATH_FIRST=true
bin/hive --hiveconf hive.root.logger=DEBUG,console
our requirement is using spark with hiveserver2 in a secure way (with authentication and authorization), currently SparkSQL alone can not provide this, we are using ranger/sentry + Hive on Spark.
hope this can help you to get a better idea which direction you should go.
here is related answer I find in the hive official site:
1.3 Comparison with Shark and Spark SQL
There are two related projects in the Spark ecosystem that provide Hive QL support on Spark: Shark and Spark SQL.
●The Shark project translates query plans generated by Hive into its own representation and executes them over Spark.
●Spark SQL is a feature in Spark. It uses Hive’s parser as the frontend to provide Hive QL support. Spark application developers can easily express their data processing logic in SQL, as well as the other Spark operators, in their code. Spark SQL supports a different use case than Hive.
Compared with Shark and Spark SQL, our approach by design supports all existing Hive features, including Hive QL (and any future extension), and Hive’s integration with authorization, monitoring, auditing, and other operational tools.
3. Hive-Level Design
As noted in the introduction, this project takes a different approach from that of Shark or Spark SQL in the sense that we are not going to implement SQL semantics using Spark's primitives. On the contrary, we will implement it using MapReduce primitives. The only new thing here is that these MapReduce primitives will be executed in Spark. In fact, only a few of Spark's primitives will be used in this design.
The approach of executing Hive’s MapReduce primitives on Spark that is different from what Shark or Spark SQL does has the following direct advantages:
1.Spark users will automatically get the whole set of Hive’s rich features, including any new features that Hive might introduce in the future.
2.This approach avoids or reduces the necessity of any customization work in Hive’s Spark execution engine.
3.It will also limit the scope of the project and reduce longterm maintenance by keeping Hive-on-Spark congruent to Hive MapReduce and Tez.
We use Hive (v. 1.2.1) to read with "sql like" on accumulo (v. 1.7.1) tables.
Is there any special settings what we can configure in hive or somewhere to gain our performance or stability?
If we use the hive this way is there any point for example trying out some hive indexing or whatever settings like "hive.auto.convert.join" or it works different way and not really affect in these case?
Thank you!
Obligatory: I wrote (most of) the AccumuloStorageHandler, but I am by no means a Hive expert.
The biggest gain you will probably be able to find is when you can structure your query in such a way that you can either prune the row-space (via a statement in the WHERE clause over the :rowid-mapped column). To my knowledge, there isn't much (any?) query optimization that is pushed down into Accumulo itself.
Depending on your workload, you could use Hive to generate your own "index tables" in Accumulo. If you can make a custom table that has the column you want to actively query stored in the Accumulo row, your queries should run much faster.
I am new to pig and writing java UDF for different operations which already exists in builtin package but the datatype does not match when called from application.
So I need to wrap pig built in functions of correct datatype from user defined datatypes.
Please suggest.
As mentioned in the comments, the solution that you propose is not possible.
Though you did not ask this (and did not provide relevant information to enable people to be more specific), it is probably possible to solve your problem with a different solution.
How can I see different version of Hbase data in Hive.
As per my understanding using HbaseStorageHandler only latest version of Hbase data will be available in Hive .Is my understanding correct/updated?
Is there any way to access different version of Hbase data using Hive??
Thanks in advance :)
(New to Hbase-Hive Integration)
That would depend on the version of hive that you are using.
Prior to hive 1.1, hbase timestamps were not accessible through the hive-hbase integration [1] (Related: [2]).
So the answer being, You require hive 1.1 or higher.
Hope it helps.
[1] https://issues.apache.org/jira/browse/HIVE-2828
[2] https://issues.apache.org/jira/browse/HIVE-8267
Not 100% answer but directions. In normal life HBase is always about special cases.
Here is slightly outdated but really simple article to understand approach:
http://hortonworks.com/blog/hbase-via-hive-part-1/
So practically you can implement any InputFormat or OutputFormat you need.
But this is related to MapReduce gears.
In principle Spark can always rely on InputFormat too so the question is only about your special case.
Another good idea is depicted here: http://www.slideshare.net/HBaseCon/ecosystem-session-3a
So snapshots could help to take state of tables you really need and then you are free to use any gear to connect Hive with HBase if it follow standards.
In general basic idea is to tune gears which connects Hive to your HBase data so they will apply needed version filters to you. This does not depend so much on versions as this interface is pretty stable.
Hope this will help you.
Is it possible to use Hive for querying Lucene index which is distributed over Hadoop???
Hadapt is a startup whose software bridges Hadoop with a SQL front-end (like Hive) and hybrid storage engines. They offer a archival text search capability that may meet your needs.
Disclaimer: I work for Hadapt.
As far as I know you can essentially write custom "row-extraction" code in Hive so I would guess that you could. I've never used Lucene and barely used Hive, so I can't be sure. If you find a more conclusive answer to your question, please post it!
I know this is a fairly old post, but thought I could offer a better alternative.
In your case, instead of going through the hassle of mapping your HDFS Lucene index to hive schema, it's better to push them into pig, because pig can read flat files. Unless you want a Relational way of storing your data, you could probably process them through Pig and use, Hbase as your DB.
You could write a custom input format for Hive to access lucene index in Hadoop.