Apache Hive 3 architectural overview
Understanding Apache Hive 3 major design features, such as default ACID transaction processing, can help you use Hive to address the growing needs of enterprise data warehouse systems.
Apache Tez is the Hive execution engine for the Hive on Tez service, which includes HiveServer (HS2) in Cloudera Manager. MapReduce is not supported. In a Cloudera cluster, if a legacy script or application specifies MapReduce for execution, an exception occurs. Most user-defined functions (UDFs) require no change to execute on Tez instead of MapReduce.
With expressions of directed acyclic graphs (DAGs) and data transfer primitives, execution of Hive queries on Tez instead of MapReduce improves query performance. In Cloudera Data Platform (CDP), Tez is usually used only by Hive, and launches and manages Tez AM automatically when Hive on Tez starts. SQL queries you submit to Hive are executed as follows:
- Hive compiles the query.
- Tez executes the query.
- Resources are allocated for applications across the cluster.
- Hive updates the data in the data source and returns query results.
Hive on Tez runs tasks on ephemeral containers and uses the standard YARN shuffle service.
Data storage and access control
One of the major architectural changes to support Hive 3 design gives Hive much more control over metadata memory resources and the file system, or object store. The following architectural changes from Hive 2 to Hive 3 provide improved security:
- Tightly controlled file system and computer memory resources, replacing flexible boundaries: Definitive boundaries increase predictability. Greater file system control improves security.
- Optimized workloads in shared files and YARN containers
- Hive uses ACID to determine which files to read rather than relying on the storage system.
- In Hive 3, file movement is reduced from that in Hive 2.
- Hive caches metadata and data agressively to reduce file system operations
The supported authorization model for Hive is Ranger. Hive enforces access controls specified in Ranger. This model offers stronger security than other security schemes and more flexibility in managing policies.
This model permits only Hive to access the data warehouse.
You can deploy new Hive application types by taking advantage of the following transaction processing characteristics:
- Mature versions of ACID transaction processing:
ACID tables are the default table type.
ACID enabled by default causes no performance or operational overload.
- Simplified application development, operations with strong transactional guarantees, and
simple semantics for SQL commands
You do not need to bucket ACID tables.
- Materialized view rewrites
- Automatic query cache
- Advanced optimizations
Hive client changes
CDP Private Cloud Base supports the thin client Beeline for working on
the command line. You can run Hive administrative commands from the command line. Beeline uses a
JDBC connection to Hive on Tez to execute commands. Parsing, compiling, and executing operations
occur in Hive on Tez. Beeline supports many of the command-line options that Hive CLI supported.
Beeline does not support
hive -e set key=value to configure the Hive
You enter supported Hive CLI commands by invoking Beeline using the
keyword, command option, and command. For example,
hive -e set. Using Beeline
instead of the thick client Hive CLI, which is no longer supported, has several advantages,
including low overhead. Beeline does not use the entire Hive code base. A small number of
daemons required to run queries simplifies monitoring and debugging.
Hive enforces allowlist and denylist settings that you can change using SET commands. Using the denylist, you can restrict memory configuration changes to prevent instability. Different Hive instances with different allowlists and denylists to establish different levels of stability.
Apache Hive Metastore sharing
Hive, Impala, and other components can share a remote Hive metastore. In CDP Public Cloud, HMS uses a pre-installed MySQL database. You perform little, or no, configuration of HMS in the cloud.
Spark and Hive tables interoperate using the Hive Warehouse Connector.
You can access ACID and external tables from Spark using the Hive Warehouse Connector. You do not need the Hive Warehouse Connector to read Hive external tables from Spark and write Hive external tables from Spark. You do not need HWC to read or write Hive external tables. Spark users just read from or write to Hive directly. You can read Hive external tables in ORC or Parquet formats. You can write Hive external tables in ORC format only. (See link below.)
Query execution of batch and interactive workloads
You can connect to Hive using a JDBC command-line tool, such as Beeline, or using an JDBC/ODBC driver with a BI tool, such as Tableau. Clients communicate with an instance of the same Hive on Tez version. You configure the settings file for each instance to perform either batch or interactive processing.