Apache Impala – Introduction

Apache Impala allows you to run SQL queries on data stored in major Apache Hadoop file formats with high performance and low latency. Rather than the large batch tasks generally associated with SQL-on-Hadoop technology, the quick query response allows interactive exploration and fine-tuning of analytic queries.

Impala interfaces with the Apache Hive metastore database, allowing both components to share databases and tables. Because of the high level of integration with Hive and compatibility with the HiveQL syntax, you can build tables, run queries, load data, and so on using either Impala or Hive.

Impala has a number of major advantages, including the following:

  • Impala interfaces with the existing CDH ecosystem, allowing data to be saved, shared, and accessed through the CDH ecosystem’s many solutions. This also prevents data silos and reduces the cost of data migration.
  • Impala allows you to access data stored in CDH without having to know Java, which is essential for MapReduce processes. Impala can read data from the HDFS file system directly. Impala also has a SQL front-end for interacting with data stored in the HBase database system or the Amazon Simple Storage System (S3).
  • Impala queries often yield responses in seconds or minutes, compared than the many minutes or hours that Hive queries frequently take.
  • Impala is a pioneer in the usage of the Parquet file format, which is a columnar storage architecture intended for large-scale queries like those found in data warehouses.

It’s made up of various daemon processes that operate on different hosts in the CDH cluster.

  • The Impala Daemon: It aids in data file reads and writes, takes queries sent through the impala-shell command, Hue, JDBC, or ODBC, parallelizes queries and distributes work across the cluster, and sends intermediate query results back to the central coordinator.
  • The Impala Statestore: It continuously monitors the health of all Impala daemons in a cluster and transmits its observations to each of them. It is physically represented by the statestored daemon process. In a cluster, such a process is only required on one server. If an Impala daemon goes offline due to a hardware failure, a network error, a software issue, or any other reason, the StateStore notifies all other Impala daemons, allowing subsequent queries to avoid making calls to the unavailable Impala daemon.
  • The Impala Catalog Service: The Catalog Service component of Impala distributes metadata changes from Impala SQL statements to all Impala daemons in a cluster. Catalogd is a daemon process that physically represents it. In a cluster, such a process is only required on one server. It makes sense to run the statestored and catalogd services on the same host because the requests are passed through the StateStore daemon.When metadata changes are made using Impala statements, the catalog service eliminates the need for REFRESH and INVALIDATE METADATA statements. You must issue REFRESH or INVALIDATE METADATA on an Impala node before executing a query there when you construct a table, load data, and so on through Hive.

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