Hive Connector

Overview

The Hive connector allows querying data stored in a Hive data warehouse. Hive is a combination of three components:

• Data files in varying formats that are typically stored in the Hadoop Distributed File System (HDFS) or in Amazon S3.

• Metadata about how the data files are mapped to schemas and tables. This metadata is stored in a database such as MySQL and is accessed via the Hive metastore service.

• A query language called HiveQL. This query language is executed on a distributed computing framework such as MapReduce or Tez.

Presto only uses the first two components: the data and the metadata. It does not use HiveQL or any part of Hive’s execution environment.

Supported File Types

The following file types are supported for the Hive connector:

• ORC

• Parquet

• Avro

• RCFile

• SequenceFile

• JSON

• Text

Configuration

The Hive connector supports Apache Hadoop 2.x and derivative distributions including Cloudera CDH 5 and Hortonworks Data Platform (HDP).

Create etc/catalog/hive.properties with the following contents to mount the hive-hadoop2 connector as the hive catalog, replacing example.net:9083 with the correct host and port for your Hive metastore Thrift service:

connector.name=hive-hadoop2
hive.metastore.uri=thrift://example.net:9083

Multiple Hive Clusters

You can have as many catalogs as you need, so if you have additional Hive clusters, simply add another properties file to etc/catalog with a different name (making sure it ends in .properties). For example, if you name the property file sales.properties, Presto will create a catalog named sales using the configured connector.

HDFS Configuration

For basic setups, Presto configures the HDFS client automatically and does not require any configuration files. In some cases, such as when using federated HDFS or NameNode high availability, it is necessary to specify additional HDFS client options in order to access your HDFS cluster. To do so, add the hive.config.resources property to reference your HDFS config files:

hive.config.resources=/etc/hadoop/conf/core-site.xml,/etc/hadoop/conf/hdfs-site.xml

Only specify additional configuration files if necessary for your setup. We also recommend reducing the configuration files to have the minimum set of required properties, as additional properties may cause problems.

The configuration files must exist on all Presto nodes. If you are referencing existing Hadoop config files, make sure to copy them to any Presto nodes that are not running Hadoop.

HDFS Username

When not using Kerberos with HDFS, Presto will access HDFS using the OS user of the Presto process. For example, if Presto is running as nobody, it will access HDFS as nobody. You can override this username by setting the HADOOP_USER_NAME system property in the Presto JVM Config, replacing hdfs_user with the appropriate username:

-DHADOOP_USER_NAME=hdfs_user

Accessing Hadoop clusters protected with Kerberos authentication

Kerberos authentication is supported for both HDFS and the Hive metastore. However, Kerberos authentication by ticket cache is not yet supported.

The properties that apply to Hive connector security are listed in the Hive Configuration Properties table. Please see the Hive Security Configuration section for a more detailed discussion of the security options in the Hive connector.

Hive Configuration Properties

Metastore Configuration Properties

The required Hive metastore can be configured with a number of properties.

AWS Glue Catalog Configuration Properties

Amazon S3 Configuration

The Hive Connector can read and write tables that are stored in S3. This is accomplished by having a table or database location that uses an S3 prefix rather than an HDFS prefix.

Presto uses its own S3 filesystem for the URI prefixes s3://, s3n:// and s3a://.

S3 Configuration Properties

S3 Credentials

If you are running Presto on Amazon EC2 using EMR or another facility, it is highly recommended that you set hive.s3.use-instance-credentials to true and use IAM Roles for EC2 to govern access to S3. If this is the case, your EC2 instances will need to be assigned an IAM Role which grants appropriate access to the data stored in the S3 bucket(s) you wish to use. It’s also possible to configure an IAM role with hive.s3.iam-role that will be assumed for accessing any S3 bucket. This is much cleaner than setting AWS access and secret keys in the hive.s3.aws-access-key and hive.s3.aws-secret-key settings, and also allows EC2 to automatically rotate credentials on a regular basis without any additional work on your part.

Custom S3 Credentials Provider

You can configure a custom S3 credentials provider by setting the Hadoop configuration property presto.s3.credentials-provider to be the fully qualified class name of a custom AWS credentials provider implementation. This class must implement the AWSCredentialsProvider interface and provide a two-argument constructor that takes a java.net.URI and a Hadoop org.apache.hadoop.conf.Configuration as arguments. A custom credentials provider can be used to provide temporary credentials from STS (using STSSessionCredentialsProvider), IAM role-based credentials (using STSAssumeRoleSessionCredentialsProvider), or credentials for a specific use case (e.g., bucket/user specific credentials). This Hadoop configuration property must be set in the Hadoop configuration files referenced by the hive.config.resources Hive connector property.

Tuning Properties

The following tuning properties affect the behavior of the client used by the Presto S3 filesystem when communicating with S3. Most of these parameters affect settings on the ClientConfiguration object associated with the AmazonS3Client.

S3 Data Encryption

Presto supports reading and writing encrypted data in S3 using both server-side encryption with S3 managed keys and client-side encryption using either the Amazon KMS or a software plugin to manage AES encryption keys.

With S3 server-side encryption, (called SSE-S3 in the Amazon documentation) the S3 infrastructure takes care of all encryption and decryption work (with the exception of SSL to the client, assuming you have hive.s3.ssl.enabled set to true). S3 also manages all the encryption keys for you. To enable this, set hive.s3.sse.enabled to true.

With S3 client-side encryption, S3 stores encrypted data and the encryption keys are managed outside of the S3 infrastructure. Data is encrypted and decrypted by Presto instead of in the S3 infrastructure. In this case, encryption keys can be managed either by using the AWS KMS or your own key management system. To use the AWS KMS for key management, set hive.s3.kms-key-id to the UUID of a KMS key. Your AWS credentials or EC2 IAM role will need to be granted permission to use the given key as well.

To use a custom encryption key management system, set hive.s3.encryption-materials-provider to the fully qualified name of a class which implements the EncryptionMaterialsProvider interface from the AWS Java SDK. This class will have to be accessible to the Hive Connector through the classpath and must be able to communicate with your custom key management system. If this class also implements the org.apache.hadoop.conf.Configurable interface from the Hadoop Java API, then the Hadoop configuration will be passed in after the object instance is created and before it is asked to provision or retrieve any encryption keys.

S3SelectPushdown

S3SelectPushdown enables pushing down projection (SELECT) and predicate (WHERE) processing to S3 Select. With S3SelectPushdown Presto only retrieves the required data from S3 instead of entire S3 objects reducing both latency and network usage.

Is S3 Select a good fit for my workload?

Performance of S3SelectPushdown depends on the amount of data filtered by the query. Filtering a large number of rows should result in better performance. If the query doesn’t filter any data then pushdown may not add any additional value and user will be charged for S3 Select requests. Thus, we recommend that you benchmark your workloads with and without S3 Select to see if using it may be suitable for your workload. By default, S3SelectPushdown is disabled and you should enable it in production after proper benchmarking and cost analysis. For more information on S3 Select request cost, please see Amazon S3 Cloud Storage Pricing.

Use the following guidelines to determine if S3 Select is a good fit for your workload:

• Your query filters out more than half of the original data set.

• Your query filter predicates use columns that have a data type supported by Presto and S3 Select. The TIMESTAMP, REAL, and DOUBLE data types are not supported by S3 Select Pushdown. We recommend using the decimal data type for numerical data. For more information about supported data types for S3 Select, see the Data Types documentation.

• Your network connection between Amazon S3 and the Amazon EMR cluster has good transfer speed and available bandwidth. Amazon S3 Select does not compress HTTP responses, so the response size may increase for compressed input files.

Considerations and Limitations

• Only objects stored in CSV format are supported. Objects can be uncompressed or optionally compressed with gzip or bzip2.

• The “AllowQuotedRecordDelimiters” property is not supported. If this property is specified, the query fails.

• Amazon S3 server-side encryption with customer-provided encryption keys (SSE-C) and client-side encryption are not supported.

• S3 Select Pushdown is not a substitute for using columnar or compressed file formats such as ORC and Parquet.

Enabling S3 Select Pushdown

You can enable S3 Select Pushdown using the s3_select_pushdown_enabled Hive session property or using the hive.s3select-pushdown.enabled configuration property. The session property will override the config property, allowing you enable or disable on a per-query basis. Non-filtering queries (SELECT * FROM table) are not pushed down to S3 Select, as they retrieve the entire object content.

For uncompressed files, using supported formats and SerDes, S3 Select scans ranges of bytes in parallel. The scan range requests run across the byte ranges of the internal Hive splits for the query fragments pushed down to S3 Select. Parallelization is controlled by the existing hive.max-split-size property.

Understanding and Tuning the Maximum Connections

Presto can use its native S3 file system or EMRFS. When using the native FS, the maximum connections is configured via the hive.s3.max-connections configuration property. When using EMRFS, the maximum connections is configured via the fs.s3.maxConnections Hadoop configuration property.

S3 Select Pushdown bypasses the file systems when accessing Amazon S3 for predicate operations. In this case, the value of hive.s3select-pushdown.max-connections determines the maximum number of client connections allowed for those operations from worker nodes.

If your workload experiences the error Timeout waiting for connection from pool, increase the value of both hive.s3select-pushdown.max-connections and the maximum connections configuration for the file system you are using.

Alluxio Configuration

Presto can read and write tables stored in the Alluxio Data Orchestration System Alluxio, leveraging Alluxio’s distributed block-level read/write caching functionality. The tables must be created in the Hive metastore with the alluxio:// location prefix (see Running Apache Hive with Alluxio for details and examples). Presto queries will then transparently retrieve and cache files or objects from a variety of disparate storage systems including HDFS and S3.

Alluxio Client-Side Configuration

To configure Alluxio client-side properties on Presto, append the Alluxio configuration directory (${ALLUXIO_HOME}/conf) to the Presto JVM classpath, so that the Alluxio properties file alluxio-site.properties can be loaded as a resource. Update the Presto JVM Config file etc/jvm.config to include the following:

-Xbootclasspath/a:<path-to-alluxio-conf>

The advantage of this approach is that all the Alluxio properties are set in the single alluxio-site.properties file. For details, see Customize Alluxio User Properties.

Alternatively, add Alluxio configuration properties to the Hadoop configuration files (core-site.xml, hdfs-site.xml) and configure the Hive connector to use the Hadoop configuration files via the hive.config.resources connector property.

Deploy Alluxio with Presto

To achieve the best performance running Presto on Alluxio, it is recommended to collocate Presto workers with Alluxio workers. This allows reads and writes to bypass the network. See Performance Tuning Tips for Presto with Alluxio for more details.

Alluxio Catalog Service

An alternative way for Presto to interact with Alluxio is via the Alluxio Catalog Service.. The primary benefits for using the Alluxio Catalog Service are simpler deployment of Alluxio with Presto, and enabling schema-aware optimizations such as transparent caching and transformations. Currently, the catalog service supports read-only workloads.

The Alluxio Catalog Service is a metastore that can cache the information from different underlying metastores. It currently supports the Hive metastore as an underlying metastore. In for the Alluxio Catalog to manage the metadata of other existing metastores, the other metastores must be “attached” to the Alluxio catalog. To attach an existing Hive metastore to the Alluxio Catalog, simply use the Alluxio CLI attachdb command. The appropriate Hive metastore location and Hive database name need to be provided.

./bin/alluxio table attachdb hive thrift://HOSTNAME:9083 hive_db_name

Once a metastore is attached, the Alluxio Catalog can manage and serve the information to Presto. To configure the Hive connector for Alluxio Catalog Service, simply configure the connector to use the Alluxio metastore type, and provide the location to the Alluxio cluster. For example, your etc/catalog/catalog_alluxio.properties will include the following (replace the Alluxio address with the appropriate location):

connector.name=hive-hadoop2
hive.metastore=alluxio
hive.metastore.alluxio.master.address=HOSTNAME:PORT

Now, Presto queries can take advantage of the Alluxio Catalog Service, such as transparent caching and transparent transformations, without any modifications to existing Hive metastore deployments.

Table Statistics

The Hive connector automatically collects basic statistics (numFiles', ``numRows, rawDataSize, totalSize) on INSERT and CREATE TABLE AS operations.

The Hive connector can also collect column level statistics:

Automatic column level statistics collection on write is controlled by the collect_column_statistics_on_write catalog session property.

Collecting table and column statistics

The Hive connector supports collection of table and partition statistics via the ANALYZE statement. When analyzing a partitioned table, the partitions to analyze can be specified via the optional partitions property, which is an array containing the values of the partition keys in the order they are declared in the table schema:

ANALYZE hive.sales WITH (
    partitions = ARRAY[
        ARRAY['partition1_value1', 'partition1_value2'],
        ARRAY['partition2_value1', 'partition2_value2']]);

This query will collect statistics for 2 partitions with keys:

• partition1_value1, partition1_value2

• partition2_value1, partition2_value2

Schema Evolution

Hive allows the partitions in a table to have a different schema than the table. This occurs when the column types of a table are changed after partitions already exist (that use the original column types). The Hive connector supports this by allowing the same conversions as Hive:

• varchar to and from tinyint, smallint, integer and bigint

• real to double

• Widening conversions for integers, such as tinyint to smallint

In adition to the conversions above, the Hive connector does also support the following conversions when working with Parquet file format:

• integer to bigint, real and double

• bigint to real and double

Any conversion failure will result in null, which is the same behavior as Hive. For example, converting the string 'foo' to a number, or converting the string '1234' to a tinyint (which has a maximum value of 127).

Avro Schema Evolution

Presto supports querying and manipulating Hive tables with Avro storage format which has the schema set based on an Avro schema file/literal. It is also possible to create tables in Presto which infers the schema from a valid Avro schema file located locally or remotely in HDFS/Web server.

To specify that Avro schema should be used for interpreting table’s data one must use avro_schema_url table property. The schema can be placed remotely in HDFS (e.g. avro_schema_url = 'hdfs://user/avro/schema/avro_data.avsc'), S3 (e.g. avro_schema_url = 's3n:///schema_bucket/schema/avro_data.avsc'), a web server (e.g. avro_schema_url = 'http://example.org/schema/avro_data.avsc') as well as local file system. This url where the schema is located, must be accessible from the Hive metastore and Presto coordinator/worker nodes.

The table created in Presto using avro_schema_url behaves the same way as a Hive table with avro.schema.url or avro.schema.literal set.

Example:

CREATE TABLE hive.avro.avro_data (
   id bigint
 )
WITH (
   format = 'AVRO',
   avro_schema_url = '/usr/local/avro_data.avsc'
)

The columns listed in the DDL (id in the above example) will be ignored if avro_schema_url is specified. The table schema will match the schema in the Avro schema file. Before any read operation, the Avro schema is accessed so query result reflects any changes in schema. Thus Presto takes advantage of Avro’s backward compatibility abilities.

If the schema of the table changes in the Avro schema file, the new schema can still be used to read old data. Newly added/renamed fields must have a default value in the Avro schema file.

The schema evolution behavior is as follows:

• Column added in new schema: Data created with an older schema will produce a default value when table is using the new schema.

• Column removed in new schema: Data created with an older schema will no longer output the data from the column that was removed.

• Column is renamed in the new schema: This is equivalent to removing the column and adding a new one, and data created with an older schema will produce a default value when table is using the new schema.

• Changing type of column in the new schema: If the type coercion is supported by Avro or the Hive connector, then the conversion happens. An error is thrown for incompatible types.

Limitations

The following operations are not supported when avro_schema_url is set:

• CREATE TABLE AS is not supported.

• Using partitioning(partitioned_by) or bucketing(bucketed_by) columns are not supported in CREATE TABLE.

• ALTER TABLE commands modifying columns are not supported.

Procedures

Use the CALL statement to perform data manipulation or administrative tasks. Procedures must include a qualified catalog name, if your Hive catalog is called web:

CALL web.system.example_procedure()

The following procedures are available:

• system.create_empty_partition(schema_name, table_name, partition_columns, partition_values)

  Create an empty partition in the specified table.

• system.sync_partition_metadata(schema_name, table_name, mode, case_sensitive)

  Check and update partitions list in metastore. There are three modes available:

  • ADD : add any partitions that exist on the file system but not in the metastore.

  • DROP: drop any partitions that exist in the metastore but not on the file system.

  • FULL: perform both ADD and DROP.

  The case_sensitive argument is optional. The default value is true for compatibility with Hive’s MSCK REPAIR TABLE behavior, which expects the partition column names in file system paths to use lowercase (e.g. col_x=SomeValue). Partitions on the file system not conforming to this convention are ignored, unless the argument is set to false.

Extra Hidden Columns

The Hive connector exposes extra hidden metadata columns in Hive tables. You can query these columns as a part of SQL query like any other columns of the table.

• $path : Filepath for the given row data

• $file_size : Filesize for the given row

• $file_modified_time : Last file modified time for the given row

How to invalidate metastore cache?

The Hive connector exposes a procedure over JMX (com.facebook.presto.hive.metastore.CachingHiveMetastore#flushCache) to invalidate the metastore cache. You can call this procedure to invalidate the metastore cache by connecting via jconsole or jmxterm.

This is useful when the Hive metastore is updated outside of Presto and you want to make the changes visible to Presto immediately.

Currently, this procedure flushes the cache for all the tables in all the schemas. This is a known limitation and will be enhanced in the future.

How to invalidate directory list cache?

The Hive connector exposes a procedure over JMX (com.facebook.presto.hive.HiveDirectoryLister#flushCache) to invalidate the directory list cache. You can call this procedure to invalidate the directory list cache by connecting via jconsole or jmxterm.

This is useful when the files are added or deleted in the cache directory path and you want to make the changes visible to Presto immediately.

Currently, this procedure flushes all the cache entries. This is a known limitation and will be enhanced in the future.

Examples

The Hive connector supports querying and manipulating Hive tables and schemas (databases). While some uncommon operations will need to be performed using Hive directly, most operations can be performed using Presto.

Create a schema

Create a new Hive schema named web that will store tables in an S3 bucket named my-bucket:

CREATE SCHEMA hive.web
WITH (location = 's3://my-bucket/')

Create a managed table

Create a new Hive table named page_views in the web schema that is stored using the ORC file format, partitioned by date and country, and bucketed by user into 50 buckets (note that Hive requires the partition columns to be the last columns in the table):

CREATE TABLE hive.web.page_views (
  view_time timestamp,
  user_id bigint,
  page_url varchar,
  ds date,
  country varchar
)
WITH (
  format = 'ORC',
  partitioned_by = ARRAY['ds', 'country'],
  bucketed_by = ARRAY['user_id'],
  bucket_count = 50
)

Drop a partition

Drop a partition from the page_views table:

DELETE FROM hive.web.page_views
WHERE ds = DATE '2016-08-09'
  AND country = 'US'

Add an empty partition

Add an empty partition to the page_views table:

CALL system.create_empty_partition(
    schema_name => 'web',
    table_name => 'page_views',
    partition_columns => ARRAY['ds', 'country'],
    partition_values => ARRAY['2016-08-09', 'US']);

Query a table

Query the page_views table:

SELECT * FROM hive.web.page_views

List partitions

List the partitions of the page_views table:

SELECT * FROM hive.web."page_views$partitions"

Create an external table

Create an external Hive table named request_logs that points at existing data in S3:

CREATE TABLE hive.web.request_logs (
  request_time timestamp,
  url varchar,
  ip varchar,
  user_agent varchar
)
WITH (
  format = 'TEXTFILE',
  external_location = 's3://my-bucket/data/logs/'
)

Drop external table

Drop the external table request_logs. This only drops the metadata for the table. The referenced data directory is not deleted:

DROP TABLE hive.web.request_logs

Drop schema

Drop a schema:

DROP SCHEMA hive.web

Hive Connector Limitations

DELETE is only supported if the WHERE clause matches entire partitions.