Since its induction in 2008, Apache Hive is viewed as the defacto standard for intuitive SQL inquiries over petabytes of information in Hadoop.
With the fulfillment of the Stinger Initiative and the successor of Stinger.next, the Apache group has significantly enhanced Hive’s speed, scale and SQL semantics. Hive effortlessly incorporates with other basic server farm advancements utilizing a natural JDBC interface.
Hadoop was invented to sort out and store monstrous measures of information of all shapes, sizes, and arrangements. As a result of Hadoop’s “construction on read” design, a Hadoop group is an ideal store of heterogeneous information—organized and unstructured—from a huge number of sources.
Information examiners utilize Hive to question, condense, investigate and dissect that information, at that point transform it into significant business understanding.
Apache Hive bring in features that make it completely flexible & unique. Here are a few features that we have extracted after understanding it completely.
1. HBase to store Hive Metadata – The current meta store implementation is slow when tables have thousands or more partitions.
2. Long-lived daemons for query fragment execution, I/O and caching – LLAP is the new hybrid execution model that enables efficiency across queries, such as caching of columnar data, JIT-friendly operator pipelines, and reduced overhead for multiple queries including concurrent queries, as well as new performance features like asynchronous I/O, pre-fetching and multi-threaded processing.
3. HPL/SQL – Implementing Procedural SQL in Hive – The PL/HQL tool implements procedural SQL for Hive (actually any SQL-on-Hadoop implementation and any JDBC source).
4. Hive on Spark Container – When Hive job is launched by Oozie, a Hive session is created and job script is executed. Session is closed when Hive job is completed.
5. Hive-on-Spark parallel ORDER BY – This is the one of the greatest feature. It allows to manually set / force the reducer count to 1 to have it in single file. It also allows to force reducer# to 1 as spark supports parallel sorting.
Apache Hive is an SQL-like software used with Hadoop to give users the capability of performing SQL-like queries on it’s own language, HiveQL, quickly and efficiently .
With Apache Hive, users can use HiveQL or traditional MapReduce systems, depending on individual needs and preferences. Hive is particularly ideal for analyzing large datasets (petabytes) and also includes a variety of storage options.Hive is full of unique tools that allow users to quickly and efficiently perform data queries and analysis.
De-normalization
Normalization is a standard process used to model your data tables with certain rules to deal with redundancy of data and anomalies. In simpler words, if you normalize your data sets, you end up creating multiple relational tables which can be joined at the run time to produce the results. Joins are expensive and difficult operations to perform and are one of the common reasons for performance issues . Because of that, it’s a good idea to avoid highly normalized table structures because they require join queries to derive the desired metrics.
Apache TEZ
Hive can use the Apache Tez execution engine instead of the venerable Map-reduce engine. if it’s not turned on by default in your environment, use Tez by setting to ‘true’ the following in the beginning of your Hive query.
Use Vectorization
Vectorized query execution improves performance of operations like scans, aggregations, filters and joins, by performing them in batches of 1024 rows at once instead of single row each time. Introduced in Hive 0.13, this feature significantly improves query execution time, and is easily enabled with two parameters settings:
Use ORC File
Hive supports ORCfile, a new table storage format that sports fantastic speed improvements through techniques like predicate push-down, compression and more.
Using ORCFile for every HIVE table is extremely beneficial to get fast response times for your HIVE queries.
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