Impala Concepts and Architecture
The following sections provide background information to help you become productive using Cloudera Impala and its features. Where appropriate, the explanations include context to help understand how aspects of Impala relate to other technologies you might already be familiar with, such as relational database management systems and data warehouses, or other Hadoop components such as Hive, HDFS, and HBase.
Components of the Impala Server
The Impala server is a distributed, massively parallel processing (MPP) database engine. It consists of different daemon processes that run on specific hosts within your CDH cluster.
The Impala Daemon
The core Impala component is a daemon process that runs on each node of the cluster, physically represented by the impalad process. It reads and writes to data files; accepts queries transmitted from the impala-shell command, Hue, JDBC, or ODBC; parallelizes the queries and distributes work to other nodes in the Impala cluster; and transmits intermediate query results back to the central coordinator node.
You can submit a query to the Impala daemon running on any node, and that node serves as the coordinator node for that query. The other nodes transmit partial results back to the coordinator, which constructs the final result set for a query. When running experiments with functionality through the impala-shell command, you might always connect to the same Impala daemon for convenience. For clusters running production workloads, you might load-balance between the nodes by submitting each query to a different Impala daemon in round-robin style, using the JDBC or ODBC interfaces.
The Impala daemons are in constant communication with the statestore, to confirm which nodes are healthy and can accept new work.
They also receive broadcast messages from the catalogd daemon (introduced in Impala 1.2) whenever any Impala node in the cluster creates, alters, or drops any type of object, or when an INSERT or LOAD DATA statement is processed through Impala. This background communication minimizes the need for REFRESH or INVALIDATE METADATA statements that were needed to coordinate metadata across nodes prior to Impala 1.2.
Related information: Modifying Impala Startup Options, Starting Impala, Setting the Idle Query and Idle Session Timeouts for impalad, Appendix A - Ports Used by Impala, Using Impala through a Proxy for High Availability
The Impala Statestore
The Impala component known as the statestore checks on the health of Impala daemons on all the nodes in a cluster, and continuously relays its findings to each of those daemons. It is physically represented by a daemon process named statestored; you only need such a process on one node in the cluster. If an Impala node goes offline due to hardware failure, network error, software issue, or other reason, the statestore informs all the other nodes so that future queries can avoid making requests to the unreachable node.
Because the statestore's purpose is to help when things go wrong, it is not critical to the normal operation of an Impala cluster. If the statestore is not running or becomes unreachable, the other nodes continue running and distributing work among themselves as usual; the cluster just becomes less robust if other nodes fail while the statestore is offline. When the statestore comes back online, it re-establishes communication with the other nodes and resumes its monitoring function.
The Impala Catalog Service
The Impala component known as the catalog service relays the metadata changes from Impala SQL statements to all the nodes in a cluster. It is physically represented by a daemon process named catalogd; you only need such a process on one node in the cluster. Because the requests are passed through the statestore daemon, it makes sense to run the statestored and catalogd services on the same node.
This new component in Impala 1.2 reduces the need for the REFRESH and INVALIDATE METADATA statements. Formerly, if you issued CREATE DATABASE, DROP DATABASE, CREATE TABLE, ALTER TABLE, or DROP TABLE statements on one Impala node, you needed to issue INVALIDATE METADATA on any other node before running a query there, so that it would pick up the changes to schema objects. Likewise, if you issued INSERT statements on one node, you needed to issue REFRESH table_name on any other node before running a query there, so that it would recognize the newly added data files. The catalog service removes the need to issue REFRESH and INVALIDATE METADATA statements when the metadata changes are performed by statement issued through Impala; when you create a table, load data, and so on through Hive, you still need to issue REFRESH or INVALIDATE METADATA on an Impala node before executing a query there.
This feature, new in Impala 1.2, touches a number of aspects of Impala:
The REFRESH and INVALIDATE METADATA statements are no longer needed when the CREATE TABLE, INSERT, or other table-changing or data-changing operation is performed through Impala. These statements are still needed if such operations are done through Hive or by manipulating data files directly in HDFS, but in those cases the statements only need to be issued on one Impala node rather than on all nodes. See REFRESH Statement and INVALIDATE METADATA Statement for the latest usage information for those statements.
See The Impala Catalog Service for background information on the catalogd service.
In Impala 1.2.4 and higher, you can specify a table name with INVALIDATE METADATA after the table is created in Hive, allowing you to make individual tables visible to Impala without doing a full reload of the catalog metadata. Impala 1.2.4 also includes other changes to make the metadata broadcast mechanism faster and more responsive, especially during Impala startup. See New Features in Impala Version 1.2.4 for details.
Programming Impala Applications
The core development language with Impala is SQL. You can also use Java or other languages to interact with Impala through the standard JDBC and ODBC interfaces used by many business intelligence tools. For specialized kinds of analysis, you can supplement the SQL built-in functions by writing user-defined functions (UDFs) in C++ or Java.
Overview of the Impala SQL Dialect
The Impala SQL dialect is descended from the SQL syntax used in the Apache Hive component (HiveQL). As such, it is familiar to users who are already familiar with running SQL queries on the Hadoop infrastructure. Currently, Impala SQL supports a subset of HiveQL statements, data types, and built-in functions.
For users coming to Impala from traditional database backgrounds, the following aspects of the SQL dialect might seem familiar or unusual:
- Impala SQL is focused on queries and includes relatively little DML. There is no UPDATE or DELETE statement. Stale data is typically discarded (by DROP TABLE or ALTER TABLE ... DROP PARTITION statements) or replaced (by INSERT OVERWRITE statements).
- All data loading is done by INSERT statements, which typically insert data in bulk by querying from other tables. There are two variations, INSERT INTO which appends to the existing data, and INSERT OVERWRITE which replaces the entire contents of a table or partition (similar to TRUNCATE TABLE followed by a new INSERT). There is no INSERT ... VALUES syntax to insert a single row.
- You often construct Impala table definitions and data files in some other environment, and then attach Impala so that it can run real-time queries. The same data files and table metadata are shared with other components of the Hadoop ecosystem.
- Because Hadoop and Impala are focused on data warehouse-style operations on large data sets, Impala SQL includes some idioms that you might find in the import utilities for traditional database systems. For example, you can create a table that reads comma-separated or tab-separated text files, specifying the separator in the CREATE TABLE statement. You can create external tables that read existing data files but do not move or transform them.
- Because Impala reads large quantities of data that might not be perfectly tidy and predictable, it does not impose length constraints on string data types. For example, you define a database column as STRING rather than CHAR(1) or VARCHAR(64).
- For query-intensive applications, you will find familiar notions such as joins, built-in functions for processing strings, numbers, and dates, aggregate functions, subqueries, and comparison operators such as IN() and BETWEEN.
- From the data warehousing world, you will recognize the notion of partitioned tables.
- In Impala 1.2 and higher, UDFs let you perform custom comparisons and transformation logic during SELECT and INSERT...SELECT statements.
Related information: Impala SQL Language Reference
Overview of Impala Programming Interfaces
You can connect and submit requests to the Impala daemons through:
- The impala-shell interactive command interpreter.
- The Apache Hue web-based user interface.
With these options, you can use Impala in heterogeneous environments, with JDBC or ODBC applications running on non-Linux platforms. You can also use Impala on combination with various Business Intelligence tools that use the JDBC and ODBC interfaces.
Each impalad daemon process, running on separate nodes in a cluster, listens to several ports for incoming requests. Requests from impala-shell and Hue are routed to the impalad daemons through the same port. The impalad daemons listen on separate ports for JDBC and ODBC requests.
How Impala Fits Into the Hadoop Ecosystem
Impala makes use of many familiar components within the Hadoop ecosystem. Impala can interchange data with other Hadoop components, as both a consumer and a producer, so it can fit in flexible ways into your ETL and ELT pipelines.
How Impala Works with Hive
A major Impala goal is to make SQL-on-Hadoop operations fast and efficient enough to appeal to new categories of users and open up Hadoop to new types of use cases. Where practical, it makes use of existing Apache Hive infrastructure that many Hadoop users already have in place to perform long-running, batch-oriented SQL queries.
In particular, Impala keeps its table definitions in a traditional MySQL or PostgreSQL database known as the metastore, the same database where Hive keeps this type of data. Thus, Impala can access tables defined or loaded by Hive, as long as all columns use Impala-supported data types, file formats, and compression codecs.
The initial focus on query features and performance means that Impala can read more types of data with the SELECT statement than it can write with the INSERT statement. To query data using the Avro, RCFile, or SequenceFile file formats, you load the data using Hive.
The Impala query optimizer can also make use of table statistics and column statistics. Originally, you gathered this information with the ANALYZE TABLE statement in Hive; in Impala 1.2.2 and higher, use the Impala COMPUTE STATS statement instead. COMPUTE STATS requires less setup, is more reliable and faster, and does not require switching back and forth between impala-shell and the Hive shell.
Overview of Impala Metadata and the Metastore
As discussed in How Impala Works with Hive, Impala maintains information about table definitions in a central database known as the metastore. Impala also tracks other metadata for the low-level characteristics of data files:
- The physical locations of blocks within HDFS.
For tables with a large volume of data and/or many partitions, retrieving all the metadata for a table can be time-consuming, taking minutes in some cases. Thus, each Impala node caches all of this metadata to reuse for future queries against the same table.
If the table definition or the data in the table is updated, all other Impala daemons in the cluster must receive the latest metadata, replacing the obsolete cached metadata, before issuing a query against that table. In Impala 1.2 and higher, the metadata update is automatic, coordinated through the catalogd daemon, for all DDL and DML statements issued through Impala. See The Impala Catalog Service for details.
For DDL and DML issued through Hive, or changes made manually to files in HDFS, you still use the REFRESH statement (when new data files are added to existing tables) or the INVALIDATE METADATA statement (for entirely new tables, or after dropping a table, performing an HDFS rebalance operation, or deleting data files). Issuing INVALIDATE METADATA by itself retrieves metadata for all the tables tracked by the metastore. If you know that only specific tables have been changed outside of Impala, you can issue REFRESH table_name for each affected table to only retrieve the latest metadata for those tables.
How Impala Uses HDFS
Impala uses the distributed filesystem HDFS as its primary data storage medium. Impala relies on the redundancy provided by HDFS to guard against hardware or network outages on individual nodes. Impala table data is physically represented as data files in HDFS, using familiar HDFS file formats and compression codecs. When data files are present in the directory for a new table, Impala reads them all, regardless of file name. New data is added in files with names controlled by Impala.
How Impala Uses HBase
HBase is an alternative to HDFS as a storage medium for Impala data. It is a database storage system built on top of HDFS, without built-in SQL support. Many Hadoop users already have it configured and store large (often sparse) data sets in it. By defining tables in Impala and mapping them to equivalent tables in HBase, you can query the contents of the HBase tables through Impala, and even perform join queries including both Impala and HBase tables. See Using Impala to Query HBase Tables for details.