yes you can run whatever you like with the data in hdfs. keep in mind that hive makes this general access pattern just a little harder, since hive has a tendency to store data and metadata separately, with the metadata in a special metadata store (not on hdfs), and its not as easy for all systems to access hive metadata.
i am not familiar at all with tajo or drill. On Fri, Jan 30, 2015 at 8:27 PM, Samuel Marks <samuelma...@gmail.com> wrote: > Thanks for the advice > > Koert: when everything is in the same essential data-store (HDFS), can't I > just run whatever complex tools I'm whichever paradigm they like? > > E.g.: GraphX, Mahout &etc. > > Also, what about Tajo or Drill? > > Best, > > Samuel Marks > http://linkedin.com/in/samuelmarks > > PS: Spark-SQL is read-only IIRC, right? > On 31 Jan 2015 03:39, "Koert Kuipers" <ko...@tresata.com> wrote: > >> since you require high-powered analytics, and i assume you want to stay >> sane while doing so, you require the ability to "drop out of sql" when >> needed. so spark-sql and lingual would be my choices. >> >> low latency indicates phoenix or spark-sql to me. >> >> so i would say spark-sql >> >> On Fri, Jan 30, 2015 at 7:56 AM, Samuel Marks <samuelma...@gmail.com> >> wrote: >> >>> HAWQ is pretty nifty due to its full SQL compliance (ANSI 92) and >>> exposing both JDBC and ODBC interfaces. However, although Pivotal does >>> open-source >>> a lot of software <http://www.pivotal.io/oss>, I don't believe they >>> open source Pivotal HD: HAWQ. >>> >>> So that doesn't meet my requirements. I should note that the project I >>> am building will also be open-source, which heightens the importance of >>> having all components also being open-source. >>> >>> Cheers, >>> >>> Samuel Marks >>> http://linkedin.com/in/samuelmarks >>> >>> On Fri, Jan 30, 2015 at 11:35 PM, Siddharth Tiwari < >>> siddharth.tiw...@live.com> wrote: >>> >>>> Have you looked at HAWQ from Pivotal ? >>>> >>>> Sent from my iPhone >>>> >>>> On Jan 30, 2015, at 4:27 AM, Samuel Marks <samuelma...@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>. Obviously by posting here I am not >>>> expecting an unbiased answer. >>>> >>>> Seeking an SQL-on-Hadoop offering which provides: 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. >>>> Transactional support would be nice also, but is not a must-have. >>>> >>>> Essentially I want a full replacement for the more traditional RDBMS, >>>> one which can scale from 1 node to a serious Hadoop cluster. >>>> >>>> Python is my language of choice for interfacing, however there does >>>> seem to be a Python JDBC wrapper <https://spark.apache.org/sql>. >>>> >>>> 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) >>>> - Cloudera Impala >>>> >>>> <http://www.cloudera.com/content/cloudera/en/products-and-services/cdh/impala.html> >>>> (significant HiveQL support, some SQL language support, no support for >>>> indexes on its tables, importantly missing DELETE, UPDATE and INTERSECT; >>>> amongst others) >>>> - 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 >>>> >>>> >>> >>
