Presto 0.163 Documentation

5.4. Hive Connector

5.4. Hive Connector


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
  • RCFile
  • SequenceFile
  • Text


Presto includes Hive connectors for multiple versions of Hadoop:

  • hive-hadoop1: Apache Hadoop 1.x
  • hive-hadoop2: Apache Hadoop 2.x
  • hive-cdh4: Cloudera CDH 4
  • hive-cdh5: Cloudera CDH 5

Create etc/catalog/ with the following contents to mount the hive-cdh4 connector as the hive catalog, replacing hive-cdh4 with the proper connector for your version of Hadoop and with the correct host and port for your Hive metastore Thrift service:

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, 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:


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:


Accessing Hadoop clusters protected with Kerberos authentication

Kerberos authentication is currently supported for both HDFS and the Hive metastore.

However there are still a few limitations:

  • Kerberos authentication is only supported for the hive-hadoop2 and hive-cdh5 connectors.
  • 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

Property Name Description Default
hive.metastore.uri The URI(s) of the Hive metastore to connect to using the Thrift protocol. If multiple URIs are provided, the first URI is used by default and the rest of the URIs are fallback metastores. This property is required. Example: thrift:// or thrift://,thrift://  
hive.config.resources An optional comma-separated list of HDFS configuration files. These files must exist on the machines running Presto. Only specify this if absolutely necessary to access HDFS. Example: /etc/hdfs-site.xml The default file format used when creating new tables. RCBINARY
hive.compression-codec The compression codec to use when writing files. GZIP
hive.force-local-scheduling Force splits to be scheduled on the same node as the Hadoop DataNode process serving the split data. This is useful for installations where Presto is collocated with every DataNode. false
hive.respect-table-format Should new partitions be written using the existing table format or the default Presto format? true
hive.immutable-partitions Can new data be inserted into existing partitions? false
hive.max-partitions-per-writers Maximum number of partitions per writer. 100
hive.metastore.authentication.type Hive metastore authentication type. Possible values are NONE or KERBEROS. NONE
hive.metastore.service.principal The Kerberos principal of the Hive metastore service.  
hive.metastore.client.principal The Kerberos principal that Presto will use when connecting to the Hive metastore service.  
hive.metastore.client.keytab Hive metastore client keytab location.  
hive.hdfs.authentication.type HDFS authentication type. Possible values are NONE or KERBEROS. NONE
hive.hdfs.impersonation.enabled Enable HDFS end user impersonation. false
hive.hdfs.presto.principal The Kerberos principal that Presto will use when connecting to HDFS.  
hive.hdfs.presto.keytab HDFS client keytab location. See Hive Security Configuration.  
security.config-file Path of config file to use when See File Based Authorization for details.  

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://. It also uses the s3bfs:// prefix for the legacy S3 block file system (not supported for hive-hadoop2 or hive-cdh5).

S3 Configuration Properties

Property Name Description
hive.s3.use-instance-credentials Use the EC2 metadata service to retrieve API credentials (defaults to true). This works with IAM roles in EC2. Default AWS access key to use. Default AWS secret key to use.
hive.s3.endpoint The S3 storage endpoint server. This can be used to connect to an S3-compatible storage system instead of AWS.
hive.s3.signer-type Specify a different signer type for S3-compatible storage. Example: S3SignerType for v2 signer type
hive.s3.staging-directory Local staging directory for data written to S3. This defaults to the Java temporary directory specified by the JVM system property Pin S3 requests to the same region as the EC2 instance where Presto is running (defaults to false).
hive.s3.ssl.enabled Use HTTPS to communicate with the S3 API (defaults to true).
hive.s3.sse.enabled Use S3 server-side encryption (defaults to false).
hive.s3.kms-key-id If set, use S3 client-side encryption and use the AWS KMS to store encryption keys and use the value of this property as the KMS Key ID for newly created objects.
hive.s3.encryption-materials-provider If set, use S3 client-side encryption and use the value of this property as the fully qualified name of a Java class which implements the AWS SDK’s EncryptionMaterialsProvider interface. If the class also implements Configurable from the Hadoop API, the Hadoop configuration will be passed in after the object has been created.

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. This is much cleaner than setting AWS access and secret keys in the and 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 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.

Property Name Description Default
hive.s3.max-error-retries Maximum number of error retries, set on the S3 client. 10
hive.s3.max-client-retries Maximum number of read attempts to retry. 3
hive.s3.max-backoff-time Use exponential backoff starting at 1 second up to this maximum value when communicating with S3. 10 minutes
hive.s3.max-retry-time Maximum time to retry communicating with S3. 10 minutes
hive.s3.connect-timeout TCP connect timeout. 5 seconds
hive.s3.socket-timeout TCP socket read timeout. 5 seconds
hive.s3.max-connections Maximum number of simultaneous open connections to S3. 500
hive.s3.multipart.min-file-size Minimum file size before multi-part upload to S3 is used. 16 MB
hive.s3.multipart.min-part-size Minimum multi-part upload part size. 5 MB

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.

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

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).


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

WITH (location = 's3://my-bucket/')

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
  format = 'ORC',
  partitioned_by = ARRAY['ds', 'country'],
  bucketed_by = ARRAY['user_id'],
  bucket_count = 50

Drop a partition from the page_views table:

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

Query the page_views table:

SELECT * FROM hive.web.page_views

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
  format = 'TEXTFILE',
  external_location = 's3://my-bucket/data/logs/'

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 a schema:

DROP SCHEMA hive.web

Hive Connector Limitations

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