Apache hive1
0 likes340 views
Apache Hive is a tool for analyzing large, unstructured data sets using SQL-like syntax on the Hadoop platform, acting as an operational data store that enhances existing data warehouses. It allows analysts to project database structures on unstructured data and run queries, although it has limitations compared to traditional SQL databases, such as the lack of transactions and real-time updates. Hive's performance has improved significantly with the introduction of the Tez execution framework, enabling faster query times and making it a cost-effective solution for data analytics.
Apache hive1
- 1. Apache Hive Sheetal Sharma Intern At IBM Innovation Centre
- 2. Apache Hive ● Apache Hive is a tool built on top of Hadoop for analyzing large, unstructured data sets using a SQL-like syntax, thus making Hadoop accessible to legions of existing BI and corporate analytics researchers. ● Hive is fundamentally an operational data store that's also suitable for analyzing large, relatively static data sets where query time is not important.
- 3. Apache Hive ● Hive makes an excellent addition to an existing data warehouse, but it is not a replacement. Instead, using Hive to augment a data warehouse is a great way to leverage existing investments while keeping up with the data deluge. ● Hive data store brings together vast amounts of unstructured data -- such as log files, customer tweets, email messages, geo-data, and CRM interactions -- and stores them in an unstructured format on cheap commodity hardware.
- 4. Apache Hive ● Hive allows analysts to project a databaselike structure on this data, to resemble traditional tables, columns, and rows, and to write SQL- like queries over it. ● This means that different schemas may be projected over the same data sets, depending on the nature of the query, allowing the user to ask questions that weren't envisioned when the data was gathered.
- 5. Apache Hive ● Hive queries traditionally had high latency, and even small queries could take some time to run because they were transformed into map-reduce jobs and submitted to the cluster to be run in batch mode. ● long-running queries were inconvenient and troublesome to run in a multi-user environment, where a single job could dominate the cluster.
- 7. Apache Hive ● HiveQL, the query language, is based on SQL-92, it differs from SQL in some important ways due to its running on top of Hadoop. ● For instance, DDL (Data Definition Language) commands need to account for the fact that tables exist in a multi-user file system that supports multiple storage formats. ● Nevertheless, SQL users will find the HiveQL language familiar and should not have any problems adapting to it.
- 9. Hive platform architecture ● From the top down, Hive looks much like any other relational database. ● Users write SQL queries and submit them for processing, using either a command line tool that interacts directly with the database engine or by using third-party tools that communicate with the database via JDBC or ODBC. ● By using the JDBC and ODBC drivers, available for Mac and Windows, data workers can connect their favorite SQL client to Hive to browse, query, and create tables.
- 10. Working with Hive ● HiveQL was designed to ease the transition from SQL and to get data analysts up and running on Hadoop right away. ● Most BI and SQL developer tools can connect to Hive as easily as to any other database. Using the ODBC connector, users can import data and use tools like PowerPivot for Excel to explore and analyze data, making big data accessible across the organization.
- 11. Differences in HiveQL and standard SQL Hive 0.13 was designed to perform full-table scans across petabyte-scale data sets using the YARN and Tez infrastructure, so some features normally found in a relational database aren't available to the Hive user. These include transactions, cursors, prepared statements, row-level updates and deletes, and the ability to cancel a running query. The absence of these features won't significantly affect data analysis, but it might affect your ability to use existing SQL queries on a Hive cluster.
- 12. Differences in HiveQL and standard SQL In a traditional database environment, the database engine controls all reads and writes to the database. In Hive, the database tables are stored as files in the Hadoop Distributed File System (HDFS), where other applications could have modified them. Although this can be a good thing, it means that Hive can never be certain if the data being read matches the schema.
- 13. Aspects of Data Storage File formats and Compression ● Tuning Hive queries can involve making the underlying map-reduce jobs run more efficiently by optimizing the number, type, and size of the files backing the database tables. ● Hive's default storage format is text, which has the advantage of being usable by other tools. ● The disadvantage, however, is that queries over raw text files can't be easily optimized.
- 14. Hive can read and write several file formats and decompress many of them on the fly. Storage requirements and query efficiency can differ dramatically among these file formats, as can be seen in the figure below (courtesy of Hortonworks). File formats are an active area of research in the Hadoop community. Efficient file formats both reduce storage costs and increase query efficiency.
- 15. For Example ● For example, let's say you want to do a query that's not part of the built-in SQL. Without a UDF, you would have to dump a temporary table to disk, run a second tool (such as Pig or Java) for your custom query, and possibly produce a third table in HDFS that would be analyzed by Hive
- 16. Hive Query Performance Hive 0.13 is the final piece in the Stinger initiative, a community effort to improve the performance of Hive. The most significant feature of 0.13 is the ability to run queries on the new Tez execution framework. ● query times drop by half when run on Tez. ● On queries that could be cached, times dropped another 30 percent. ● On larger data sets, the speedup was even more dramatic. ● possible to execute petabyte-scale queries to refine and cleanse data for later incorporation into data warehouse analytics.
- 17. Hive Query Performance ● Hadoop and Hive could also be used in the reverse scenario: to off-load data summaries that would otherwise need to be stored in the data warehouse at much greater cost. ● Organizations or departments without a data warehouse can start with Hive to get a feel for the value of data analytics. ● It does make a great, low-cost, large-scale operational data store with a fair set of analytics tools. ● Hive offers near linear scalability in query processing, an order of magnitude better price/performance ratio than traditional enterprise data warehouses.
- 18. Apache Hive At a Glance
- 19. Thank You!