Additionally it is of key importance to use the right data types for the columns. Use int for ids, int or decimal or float or double etc for numeric values etc. - A bad data model using varchars and string where not appropriate is a significant bottle neck. Furthermore include partition columns in join statements (not where) otherwise you do a full table scan ignoring partitions
Le jeu. 6 août 2015 à 15:07, Jörn Franke <jornfra...@gmail.com> a écrit : > Yes you should use orc it is much faster and more compact. Additionally > you can apply compression (snappy) to increase performance. Your data > processing pipeline seems to be not.very optimized. You should use the > newest hive version enabling storage indexes and bloom filters on > appropriate columns. Ideally you should insert the data sorted > appropriately. Partitioning and setting the execution engine to tez is also > beneficial. > > Hbase with phoenix should currently only be used if you do few joins, not > very complex queries and not many full table scans. > > Le jeu. 6 août 2015 à 14:54, venkatesh b <venkateshmailingl...@gmail.com> > a écrit : > >> Hi, here I got two things to know. >> FIRST: >> In our project we use hive. >> We daily get new data. We need to process this new data only once. And >> send this processed data to RDBMS. Here in processing we majorly use many >> complex queries with joins with where condition and grouping functions. >> There are many intermediate tables generated around 50 while >> processing. Till now we use text format as storage. We came across ORC file >> format. I would like to know that since it is one Time querying the table >> is it worth of storing as ORC format. >> >> SECOND: >> I came to know about HBase, which is faster. >> Can I replace hive with HBase for processing of data daily faster. >> Currently it is taking 15hrs daily with hive. >> >> >> Please inform me if any other information is needed. >> >> Thanks & regards >> Venkatesh >> >
