B Hive Support

[Cara Canellas]

Ilove the ability in Liquibase to have error handling and incremental idempotency in my SQL deployment operations, both command-line and via Maven builds. Thanks to creative use of RunAlways and Preconditions I am even able to create TDD unit tests for liquibase operations that ensure certain operations run always and can be ensured to remain true as part of each code build or database deployment!

It seems plausible/feasible at a high level to me because one just needs to have Liquibase pass the DDL through a JDBC driver and read back the responses, right? I am probably grossly oversimplifying.

Yes, support for Hive changes are definitely possible. The existing 3.x extension system should allow you to plug something in but like Steve says I am busily working on larger changes with 4.x that will make it easier to support new environments.

Is it possible to keep these core tables somewhere outside in RDBMS (for ex, mysql) as common place for all Hive/Impala databases? Yes, there could be problems in maintain the integrity between these two operations.

I've been asked to set hive.txn.manager = org.apache.hadoop.hive.ql.lockmgr.DbTxnManager and hive.support.concurrency = true, because a subset of users is concerned about dirty reads on an external table while an external job runs to consolidate small files within a partition, so they want to do an exclusive lock during the consolidation....

The two properties hive.txn.manager = org.apache.hadoop.hive.ql.lockmgr.DbTxnManager and hive.support.concurrency = true are set for ACID tables. External tables cannot be ACID tables as the ACID compactor cannot control the data managed by them.

I thought those 2 settings pre-dated the introduction of ACID tables. I can understand the "External tables cannot be ACID tables..." part, but I would think those settings could be used to allow users to issue an "exclusive lock" on an external table to prevent reading from it thru hive while external jobs manipulate the underlying files....

Utilizing a support ticketing system will primarily help you organize and track customer support requests effectively, preventing any requests from being overlooked or ignored. This might lessen the workload on your support staff and increase client satisfaction.

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Error in SQL statement: QueryExecutionException: FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.DDLTask. java.lang.UnsupportedOperationException: Parquet does not support timestamp. See HIVE-6384

Many thanks! The above worked and I was able to create the table with the timestamp data type. Appreciate the automatic partition discovery also! Ill focus on using the Dataframes vs Hive implementation going forward.

This reminds me of the questions Eric Lippert gets of the form "Why doesn't C# have Java feature X?" People don't build a language by starting with another one and removing stuff, they start with nothing and decide what features to implement.

At some point, hive supported neither IN\EXISTS subqueries, nor LEFT SEMI JOIN. Then, someone suggested they add LEFT SEMI JOIN. Now that that's in the language, it takes away some of the reason for implementing IN\EXISTS subqueries, since the two are semantically equivalent.

Spark SQL also supports reading and writing data stored in Apache Hive.However, since Hive has a large number of dependencies, these dependencies are not included in thedefault Spark distribution. If Hive dependencies can be found on the classpath, Spark will load themautomatically. Note that these Hive dependencies must also be present on all of the worker nodes, asthey will need access to the Hive serialization and deserialization libraries (SerDes) in order toaccess data stored in Hive.

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.

A comma-separated list of class prefixes that should be loaded using the classloader that is shared between Spark SQL and a specific version of Hive. An example of classes that should be shared is JDBC drivers that are needed to talk to the metastore. Other classes that need to be shared are those that interact with classes that are already shared. For example, custom appenders that are used by log4j.

A comma separated list of class prefixes that should explicitly be reloaded for each version of Hive that Spark SQL is communicating with. For example, Hive UDFs that are declared in a prefix that typically would be shared (i.e. org.apache.spark.*).

Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale.Hive Metastore(HMS) provides a central repository of metadata that can easily be analyzed to make informed,data driven decisions, and therefore it is a critical component of many data lake architectures.Hive is built on top of Apache Hadoop and supports storage on S3, adls, gs etc though hdfs.Hive allows users to read, write, and manage petabytes of data using SQL.

The Hive Metastore (HMS) is a central repository of metadata for Hive tables and partitions in a relational database,and provides clients (including Hive, Impala and Spark) access to this information using the metastore service API.It has become a building block for data lakes that utilize the diverse world of open-source software, such as Apache Spark and Presto.In fact, a whole ecosystem of tools, open-source and otherwise, are built around the Hive Metastore, some of which this diagram illustrates.

Apache Hive enables interactive and subsecond SQL through Low Latency Analytical Processing (LLAP),introduced in Hive 2.0 that makes Hive faster by using persistent query infrastructure and optimized data caching

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Some beekeepers who use solid floors tilt the hive so any moisture can drain out of the entrance, rather than pooling at the back of the hive. This is clearly irrelevant for those of us who use open mesh floors.

To support the longitudinal hive rails I built lateral supports from 4 x 2 offcuts. I drilled a 40 mm hole through them to take the scaffold jack screw thread. I used a centre distance of 50 cm, leaving exactly 46 cm to accommodate a National hive. In retrospect, making these rail supports a bit longer would have provided a wider, and therefore more stable, base 16.

The top of the scaffold jack screw thread is designed to fit within a scaffold pipe. It is therefore unfinished and mine had very rough edges. Without modification this would result in lacerations to my bee suit and permanent scarring to my hands.

Ninety seven cappuccinos later I had the four milk bottle tops necessary for the legs on one stand. Not only do these prevent shredding your bee suit, gloves and flesh, but they also stop water running down inside the leg 18.

The issue is that we need to use partitioning on both Postgres and Hive. At moment I can define custom DDL clauses on Entity level in ER diagram, but I need support to distinguish to execute these custom clauses per technology level (Hive is different definition and Postgres as well.)

To enable Iceberg support in Hive, the HiveIcebergStorageHandler and supporting classes need to be made available onHive's classpath. These are provided by the iceberg-hive-runtime jar file. For example, if using the Hive shell, thiscan be achieved by issuing a statement like so:

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