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Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query and analysis. To accelerate queries, it provides indexes, including bitmap indexes.
HiveQL offers extensions not in SQL, including multitable inserts and create table as select , but only offers basic support for indexes. HiveQL lacked support for transactions and materialized views , and only limited subquery support. Internally, a compiler translates HiveQL statements into a directed acyclic graph of MapReduce , Tez, or Spark jobs, which are submitted to Hadoop for execution.
The word count program counts the number of times each word occurs in the input. The word count can be written in HiveQL as: Checks if table docs exists and drops it if it does. This query serves to split the input words into different rows of a temporary table aliased as temp. This results in the count column holding the number of occurrences for each word of the word column.
The storage and querying operations of Hive closely resemble those of traditional databases. While Hive is a SQL dialect, there are a lot of differences in structure and working of Hive in comparison to relational databases. The differences are mainly because Hive is built on top of the Hadoop ecosystem, and has to comply with the restrictions of Hadoop and MapReduce. A schema is applied to a table in traditional databases. In such traditional databases, the table typically enforces the schema when the data is loaded into the table.
This enables the database to make sure that the data entered follows the representation of the table as specified by the table definition.
This design is called schema on write. In comparison, Hive does not verify the data against the table schema on write.
Instead, it subsequently does run time checks when the data is read. This model is called schema on read. Checking data against table schema during the load time adds extra overhead, which is why traditional databases take a longer time to load data. Quality checks are performed against the data at the load time to ensure that the data is not corrupt.
Early detection of corrupt data ensures early exception handling. Hive, on the other hand, can load data dynamically without any schema check, ensuring a fast initial load, but with the drawback of comparatively slower performance at query time.
Hive does have an advantage when the schema is not available at the load time, but is instead generated later dynamically. Transactions are key operations in traditional databases. Atomicity , Consistency , Isolation , and Durability. Transactions in Hive were introduced in Hive 0. This is because Hadoop does not support row level updates over specific partitions. These partitioned data are immutable and a new table with updated values has to be created.
Hadoop began using Kerberos authorization support to provide security. Kerberos allows for mutual authentication between client and server. The previous versions of Hadoop had several issues such as users being able to spoof their username by setting the hadoop. TaskTracker jobs are run by the user who launched it and the username can no longer be spoofed by setting the hadoop. The Hadoop distributed file system authorization model uses three entities: The default permissions for newly created files can be set by changing the umask value for the Hive configuration variable hive.
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