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To configure the Hive connector, create a catalog properties fileetc/catalog/example.properties that references the hiveconnector and defines a metastore. You must configure a metastore for tablemetadata. If you are using a Hive metastore,hive.metastore.uri must be configured:

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For basic setups, Trino configures the HDFS client automatically anddoes not require any configuration files. In some cases, such as when usingfederated HDFS or NameNode high availability, it is necessary to specifyadditional HDFS client options in order to access your HDFS cluster. To do so,add the hive.config.resources property to reference your HDFS config files:

Before running any CREATE TABLE or CREATE TABLE AS statementsfor Hive tables in Trino, you must check that the user Trino isusing to access HDFS has access to the Hive warehouse directory. The Hivewarehouse directory is specified by the configuration variablehive.metastore.warehouse.dir in hive-site.xml, and the defaultvalue is /user/hive/warehouse.

Can new data be inserted into existing partitions? If true then settinghive.insert-existing-partitions-behavior to APPEND is not allowed. Thisalso affects the insert_existing_partitions_behavior session property inthe same way.

Controls whether the temporary staging directory configured athive.temporary-staging-directory-path is used for write operations.Temporary staging directory is never used for writes to non-sorted tables onS3, encrypted HDFS or external location. Writes to sorted tables willutilize this path for staging temporary files during sorting operation. Whendisabled, the target storage will be used for staging while writing sortedtables which can be inefficient when writing to object stores like S3.

The table file format. Valid values include ORC, PARQUET, AVRO,RCBINARY, RCTEXT, SEQUENCEFILE, JSON, TEXTFILE, CSV, andREGEX. The catalog property hive.storage-format sets the default valueand can change it to a different default.

For the Hive connector, a table scan can be delayed for a configured amount oftime until the collection of dynamic filters by using the configuration propertyhive.dynamic-filtering.wait-timeout in the catalog file or the catalogsession property .dynamic_filtering_wait_timeout.

Apache Hive is a database built on top of Hadoop and facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in Hadoop compatible distributed file system. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL.Please DO NOT use this tag for flutter database which is also named Hive, use flutter-hive tag instead.

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When working with Hive, one must instantiate SparkSession with Hive support, includingconnectivity to a persistent Hive metastore, support for Hive serdes, and Hive user-defined functions.Users who do not have an existing Hive deployment can still enable Hive support. When not configuredby the hive-site.xml, the context automatically creates metastore_db in the current directory andcreates a directory configured by spark.sql.warehouse.dir, which defaults to the directoryspark-warehouse in the current directory that the Spark application is started. Note thatthe hive.metastore.warehouse.dir property in hive-site.xml is deprecated since Spark 2.0.0.Instead, use spark.sql.warehouse.dir to specify the default location of database in warehouse.You may need to grant write privilege to the user who starts the Spark application.

Transactions are key operations in traditional databases. As any typical RDBMS, Hive supports all four properties of transactions (ACID): Atomicity, Consistency, Isolation, and Durability. Transactions in Hive were introduced in Hive 0.13 but were only limited to the partition level.[29] The recent version of Hive 0.14 had these functions fully added to support complete ACID properties. Hive 0.14 and later provides different row level transactions such as INSERT, DELETE and UPDATE.[30] Enabling INSERT, UPDATE, and DELETE transactions require setting appropriate values for configuration properties such as hive.support.concurrency, hive.enforce.bucketing, and hive.exec.dynamic.partition.mode.[31]

Hive v0.7.0 added integration with Hadoop security. Hadoop began using Kerberos authorization support to provide security. Kerberos allows for mutual authentication between client and server. In this system, the client's request for a ticket is passed along with the request. The previous versions of Hadoop had several issues such as users being able to spoof their username by setting the hadoop.job.ugi property and also MapReduce operations being run under the same user: Hadoop or mapred. With Hive v0.7.0's integration with Hadoop security, these issues have largely been fixed. TaskTracker jobs are run by the user who launched it and the username can no longer be spoofed by setting the hadoop.job.ugi property. Permissions for newly created files in Hive are dictated by the HDFS. The Hadoop distributed file system authorization model uses three entities: user, group and others with three permissions: read, write and execute. The default permissions for newly created files can be set by changing the unmask value for the Hive configuration variable hive.files.umask.value.[5] ff782bc1db