Comparing Stinger to Impala

With Hadoop 2.0 and the new additions of Stinger and Impala I did a (not representive) test of the performance on a Virtual Box running on my desktop computer. It was using the following setup:

  • 4 GB RAM
  • Intel Core i5 2500 3.3 GHz

The datasets were the following:

  1. Dataset 1: 71.386.291 rows and 5 columns
  2. Dataset 2: 132.430.086 rows and 4 columns
  3. Dataset 3: partitioned data of 2.153.924 rows and 32 columns
  4. Dataset 4: unpartitioned data of 2.153.924 rows and 32 columns

The results were the following:

QueryHive (0.10.0)ImpalaStinger (Hive 0.12.0)
Join tables167.61 sec31.46 sec122.58 sec
Partitioned tables Dataset 342.45 sec0.29 sec20.97 sec
Unpartitioned tables Dataset 447.92 sec1.20 sec36.46 sec
Grouped Select Dataset 1533.83 sec81.11 sec444.634 sec
Grouped Select Dataset 2323.56 sec49.72 sec313.98 sec
Count Dataset 1252.56 sec66.48 sec243.91 sec
Count Dataset 2158.93 sec41.64 sec174.46 sec
Compare Impala vs. Stinger
Compare Impala vs. Stinger

This shows that Stinger provides a faster SQL interface on Hive, but since it is still using Map / Reduce when calculating data it is no match for Impala that doesn’t use Map / Reduce. So using Impala makes sense when you want to analyse data in Hadoop using SQL even on a small installation. This should give you easy and fast access to all data stored in your Hadoop cluster, that was before not possible.
Facebook’s Presto should achieve nearly the same results, since the underlying technique is similar. These latest additions and changes to the Hadoop framework really seem like a big boost in making this project more accessible for many people.

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Hadoop and MPP

With Big Data Map/Reduce is always the first term that comes into mind. But it’s not the only way to handle large amounts of data. There are databasesystems especially built to deal with huge amounts of data and they are called Massively Parallel Processing (MPP) databases.
MPP database systems have been around for a longer time than Map/Reduce and its most popular integration Hadoop and are based on a shared nothing architecture. The data is partitioned across severel nodes of hardware and queries are processed via network interconnect on a central server. They often use commodity hardware that is as inexpensive as hardware for Map/Reduce. For working with data they have the advantage to make use of SQL as their interface, the language used by most Data Scientists and other analytic prefessionals so far.
Map/Reduce provides a Java interface to analyse the data, which comes with more time to implement than just write an SQL statement. Hadoop has some projects, that provide a SQL similar query language, like Hive which provides HiveQL, a SQL like query language, as interface.
Since both systems handle data, there will be a lot gained, when both are combined. There are already projects working on that, like Aster Data nCluster or Teradata and Hortonworks.
There is even a new product bringing both worlds together as one product, Hadapt. With this product you can access all your data, structured or unstructured, in a single plattform. Each node has space for SQL as well as for Map/Reduce.

Last but not least a list of some MPP databases available right now:

Depending on your business needs, you may not need a Map/Reduce cluster, but a MPP database, or both to benefit from their respective strenghts in your implementation.

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