Impala Architecture

What is Apache Impala?

Apache Impala is an open-source, distributed SQL query engine for data stored in Apache Hadoop and Apache Hadoop-based systems, such as Apache Kudu and Apache HBase. It allows users to issue low-latency SQL queries to data stored in these systems, using SQL syntax similar to that of traditional relational database management systems.

Impala architecture consists of the following main components:

  1. Impala Daemons: Impala consists of several daemons, including the Impala Server, Impala Statestore, and Impala Catalog Server. The Impala Server is responsible for executing queries and returning results to clients. The Statestore maintains the current state of the Impala cluster, including the status of all Impala daemons and the metadata of the data stored in the cluster. The Catalog Server stores the metadata of the data stored in the cluster, including the schema and location of each table and partition.
  2. Impala Clients: Impala clients are programs that connect to the Impala Server to execute queries and retrieve results. Clients can be written in any programming language that supports the ODBC or JDBC APIs.
  3. Data Storage: Impala can query data stored in a variety of formats, including Apache HDFS, Apache HBase, and Apache Kudu. Impala is designed to work with data stored in HDFS, which is a distributed file system designed for storing very large data sets. HBase is a distributed, column-oriented database built on top of HDFS, and Kudu is a distributed, columnar storage engine designed for fast processing of structured data.
  4. Query Execution: When a client submits a query to Impala, the Impala Server receives the query and parses it to determine the best way to execute it. If the query requires data from multiple tables or partitions, the Impala Server will create a query plan that specifies the order in which the data should be accessed and the calculations that should be performed. The query plan is then executed by the Impala Server, which retrieves the necessary data from the data storage and performs the required calculations. The results of the query are then returned to the client.

Impala uses a distributed query execution engine, which means that it can scale out across a cluster of machines to perform queries on very large data sets. The Impala Server and the daemons that support it are designed to take advantage of the parallel processing capabilities of modern hardware and can execute queries concurrently on multiple machines.

One of the key features of Impala is its ability to perform real-time queries on data stored in Hadoop. Impala uses a variety of techniques to minimize the latency of queries, including in-memory query execution, code generation, and data partitioning. These techniques allow Impala to perform complex queries on large data sets with minimal delay.

Impala also includes support for a variety of SQL features, including subqueries, aggregations, and window functions. These features allow users to perform complex analyses of their data using familiar SQL syntax.

In summary, Impala is a distributed SQL query engine that allows users to perform real-time queries on data stored in Hadoop and Hadoop-based systems. It uses a distributed query execution engine and a variety of techniques to minimize query latency and supports a wide range of SQL features for data analysis.

Apache Hadoop is an open-source software framework for storing and processing large amounts of data distributed across a cluster of computers. It is designed to handle large volumes of structured and unstructured data and is commonly used for storing and analyzing data from various sources, such as web logs, sensor data, and social media feeds.

Hadoop consists of several components, including the Hadoop Distributed File System (HDFS), which is a distributed file system designed to store very large data sets; and the MapReduce programming model, which is a framework for writing applications that can process large amounts of data in parallel.

Hadoop is designed to be scalable and fault-tolerant. It is able to store and process large amounts of data by distributing it across a cluster of machines and can automatically recover from hardware failures.

Hadoop is often used in conjunction with other tools and frameworks, such as Apache Impala, Apache Hive, and Apache Pig, which provide additional functionality for storing, querying, and analyzing data.

See Also