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From Azuryy Yu <azury...@gmail.com>
Subject Re: Which [open-souce] SQL engine atop Hadoop?
Date Thu, 05 Feb 2015 09:39:09 GMT
please look at:
http://mail-archives.apache.org/mod_mbox/tajo-user/201502.mbox/browser



On Tue, Jan 27, 2015 at 5:13 PM, Daniel Haviv <danielrulez@gmail.com> wrote:

> Can you elaborate on why you prefer Tajo?
>
> Daniel
>
> On 27 בינו׳ 2015, at 10:35, Azuryy Yu <azuryyyu@gmail.com> wrote:
>
> You almost list all open sourced MPP real time SQL-ON-Hadoop.
>
> I prefer Tajo, which was relased by 0.9.0 recently, and still working in
> progress for 1.0
>
>
> On Mon, Jan 26, 2015 at 10:19 PM, Samuel Marks <samuelmarks@gmail.com>
> wrote:
>
>> Since Hadoop <https://hive.apache.org> came out, there have been various
>> commercial and/or open-source attempts to expose some compatibility with
>> SQL <http://drill.apache.org>.
>>
>> I am seeking one which is good for low-latency querying, and supports the
>> most common CRUD <https://spark.apache.org>, including [the basics!]
>> along these lines: CREATE TABLE, INSERT INTO, SELECT * FROM, UPDATE
>> Table SET C1=2 WHERE, DELETE FROM, and DROP TABLE.
>>
>> I will be utilising them from Python, however there does seem to be a Python
>> JDBC wrapper <https://spark.apache.org/sql>. Additionally it needs to be
>> scalable for big and small data (starting on a single-node "cluster").
>>
>> Here is what I've found thus far:
>>
>>    - Apache Hive <https://hive.apache.org> (SQL-like, with interactive
>>    SQL thanks to the Stinger initiative)
>>    - Apache Drill <http://drill.apache.org> (ANSI SQL support)
>>    - Apache Spark <https://spark.apache.org> (Spark SQL
>>    <https://spark.apache.org/sql>, queries only, add data via Hive, RDD
>>    <https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.SchemaRDD>
>>    or Paraquet <http://parquet.io/>)
>>    - Apache Phoenix <http://phoenix.apache.org> (built atop Apache HBase
>>    <http://hbase.apache.org>, lacks full transaction
>>    <http://en.wikipedia.org/wiki/Database_transaction> support, relational
>>    operators <http://en.wikipedia.org/wiki/Relational_operators> and
>>    some built-in functions)
>>    - Presto <https://github.com/facebook/presto> from Facebook (can
>>    query Hive, Cassandra <http://cassandra.apache.org>, relational DBs
>>    &etc. Doesn't seem to be designed for low-latency responses across small
>>    clusters, or support UPDATE operations. It is optimized for data
>>    warehousing or analytics¹
>>    <http://prestodb.io/docs/current/overview/use-cases.html>)
>>    - SQL-Hadoop <https://www.mapr.com/why-hadoop/sql-hadoop> via MapR
>>    community edition <https://www.mapr.com/products/hadoop-download>
>>    (seems to be a packaging of Hive, HP Vertica
>>    <http://www.vertica.com/hp-vertica-products/sqlonhadoop>, SparkSQL,
>>    Drill and a native ODBC wrapper
>>    <http://package.mapr.com/tools/MapR-ODBC/MapR_ODBC>)
>>    - Apache Kylin <http://www.kylin.io> from Ebay (provides an SQL
>>    interface and multi-dimensional analysis [OLAP
>>    <http://en.wikipedia.org/wiki/OLAP>], "… offers ANSI SQL on Hadoop
>>    and supports most ANSI SQL query functions". It depends on HDFS, MapReduce,
>>    Hive and HBase; and seems targeted at very large data-sets though maintains
>>    low query latency)
>>    - Apache Tajo <http://tajo.apache.org> (ANSI/ISO SQL standard
>>    compliance with JDBC <http://en.wikipedia.org/wiki/JDBC> driver
>>    support [benchmarks against Hive and Impala
>>    <http://blogs.gartner.com/nick-heudecker/apache-tajo-enters-the-sql-on-hadoop-space>
>>    ])
>>    - Cascading <http://en.wikipedia.org/wiki/Cascading_%28software%29>'s
>>    Lingual <http://docs.cascading.org/lingual/1.0/>²
>>    <http://docs.cascading.org/lingual/1.0/#sql-support> ("Lingual
>>    provides JDBC Drivers, a SQL command shell, and a catalog manager for
>>    publishing files [or any resource] as schemas and tables.")
>>
>> Which—from this list or elsewhere—would you recommend, and why?
>> Thanks for all suggestions,
>>
>> Samuel Marks
>> http://linkedin.com/in/samuelmarks
>>
>
>

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