13.7. 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

  • Avro

  • RCFile

  • SequenceFile

  • JSON

  • Text


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:


Use presto-admin to deploy the connector file. See Adding a Catalog.

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:


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 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.

HDFS Permissions

Before running any CREATE TABLE or CREATE TABLE ... AS statements for Hive tables in Presto, you need to check that the operating system user running the Presto server has access to the Hive warehouse directory on HDFS. The Hive warehouse directory is specified by the configuration variable hive.metastore.warehouse.dir in hive-site.xml, and the default value is /user/hive/warehouse. If that is not the case, either add the following to jvm.config on all of the nodes: -DHADOOP_USER_NAME=USER, where USER is an operating system user that has proper permissions for the Hive warehouse directory, or start the Presto server as a user with similar permissions. The hive user generally works as USER, since Hive is often started with the hive user. If you run into HDFS permissions problems on CREATE TABLE ... AS, remove /tmp/presto-* on HDFS, fix the user as described above, then restart all of the Presto servers.

Hive Configuration Properties

Property Name




The type of Hive metastore to use. Presto currently supports the default Hive Thrift metastore (thrift), and the AWS Glue Catalog (glue) as metadata sources.



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


Enable reading data from subdirectories of table or partition locations. If disabled, subdirectories are ignored. This is equivalent to the hive.mapred.supports.subdirectories property in Hive.



The default file format used when creating new tables.



The compression codec to use when writing files.



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.



Should new partitions be written using the existing table format or the default Presto format?



Can new data be inserted into existing partitions?



Should empty files be created for buckets that have no data?



Maximum number of partitions per writer.



Maximum number of partitions for a single table scan.



HDFS authentication type. Possible values are NONE or KERBEROS.



Enable HDFS end user impersonation.



The Kerberos principal that Presto will use when connecting to HDFS.


HDFS client keytab location.


See Hive Security Configuration.


Path of config file to use when hive.security=file. See File Based Authorization for details.


Enable writes to non-managed (external) Hive tables.



Enable creating non-managed (external) Hive tables.



Enables automatic column level statistics collection on write. See Table Statistics for details.



Enable query pushdown to AWS S3 Select service.



Maximum number of simultaneously open connections to S3 for S3 Select Pushdown.


Hive Thrift Metastore Configuration Properties

Property Name



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://


The username Presto will use to access the Hive metastore.


Hive metastore authentication type. Possible values are NONE or KERBEROS (defaults to NONE).


The Kerberos principal of the Hive metastore service.


Enable Metastore end user impersonation.


The Kerberos principal that Presto will use when connecting to the Hive metastore service.


Hive metastore client keytab location.

AWS Glue Catalog Configuration Properties

Property Name



AWS region of the Glue Catalog. This is required when not running in EC2, or when the catalog is in a different region. Example: us-east-1


Pin Glue requests to the same region as the EC2 instance where Presto is running (defaults to false).


Max number of concurrent connections to Glue (defaults to 5).


Hive Glue metastore default warehouse directory


AWS access key to use to connect to the Glue Catalog. If specified along with hive.metastore.glue.aws-secret-key, this parameter takes precedence over hive.metastore.glue.iam-role.


AWS secret key to use to connect to the Glue Catalog. If specified along with hive.metastore.glue.aws-access-key, this parameter takes precedence over hive.metastore.glue.iam-role.


ARN of an IAM role to assume when connecting to the Glue Catalog.

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

Property Name



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.


IAM role to assume.


The S3 storage endpoint server. This can be used to connect to an S3-compatible storage system instead of AWS. When using v4 signatures, it is recommended to set this to the AWS region-specific endpoint (e.g., http[s]://<bucket>.s3-<AWS-region>.amazonaws.com).


Specify a different signer type for S3-compatible storage. Example: S3SignerType for v2 signer type


Use path-style access for all requests to the S3-compatible storage. This is for S3-compatible storage that doesn’t support virtual-hosted-style access. (defaults to false)


Local staging directory for data written to S3. This defaults to the Java temporary directory specified by the JVM system property java.io.tmpdir.


Pin S3 requests to the same region as the EC2 instance where Presto is running (defaults to false).


Use HTTPS to communicate with the S3 API (defaults to true).


Use S3 server-side encryption (defaults to false).


The type of key management for S3 server-side encryption. Use S3 for S3 managed or KMS for KMS-managed keys (defaults to S3).


The KMS Key ID to use for S3 server-side encryption with KMS-managed keys. If not set, the default key is used.


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.


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.


Canned ACL to use while uploading files to S3 (defaults to Private).


Ignore Glacier objects rather than failing the query. This will skip data that may be expected to be part of the table or partition. Defaults to false.

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.

Property Name




Maximum number of error retries, set on the S3 client.



Maximum number of read attempts to retry.



Use exponential backoff starting at 1 second up to this maximum value when communicating with S3.

10 minutes


Maximum time to retry communicating with S3.

10 minutes


TCP connect timeout.

5 seconds


TCP socket read timeout.

5 seconds


Maximum number of simultaneous open connections to S3.



Minimum file size before multi-part upload to S3 is used.

16 MB


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.

S3 Select Pushdown

S3 Select Pushdown enables pushing down projection (SELECT) and predicate (WHERE) processing to S3 Select. With S3 Select Pushdown, 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 S3 Select Pushdown 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, S3 Select Pushdown 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.

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.

Google Cloud Storage Configuration

The Hive connector can access data stored in GCS, using the gs:// URI prefix. Please refer to the Hive Connector GCS Tutorial for step-by-step instructions.

GCS Configuration properties

Property Name



JSON key file used to authenticate with Google Cloud Storage.


Use client-provided OAuth token to access Google Cloud Storage. This is mutually exclusive with a global JSON key file.

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:

Column Type

Collectible Statistics


number of nulls, number of distinct values, min/max values


number of nulls, number of distinct values, min/max values


number of nulls, number of distinct values, min/max values


number of nulls, number of distinct values, min/max values


number of nulls, number of distinct values, min/max values


number of nulls, number of distinct values, min/max values


number of nulls, number of distinct values, min/max values


number of nulls, number of distinct values, min/max values


number of nulls, number of distinct values, min/max values


number of nulls, number of distinct values


number of nulls, number of distinct values


number of nulls


number of nulls, number of true/false values

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

Updating table and partition statistics

If your queries are complex and include joining large data sets, running ANALYZE on tables/partitions may improve query performance by collecting statistical information about the data.

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 table_name WITH (
    partitions = ARRAY[
        ARRAY['p1_value1', 'p1_value2'],
        ARRAY['p2_value1', 'p2_value2']])

This query will collect statistics for two partitions with keys p1_value1, p1_value2 and p2_value1, p2_value2.

On wide tables, collecting statistics for all columns can be expensive and can have a detrimental effect on query planning. It is also typically unnecessary - statistics are only useful on specific columns, like join keys, predicates, grouping keys. One can specify a subset of columns to be analyzed via the optional columns property:

ANALYZE table_name WITH (
    partitions = ARRAY[ARRAY['p2_value1', 'p2_value2']],
    columns = ARRAY['col_1', 'col_2'])

This query will collect statistics for columns col_1 and col_2 for the partition with keys p2_value1, p2_value2.

Note that if statistics were previously collected for all columns, they need to be dropped before re-analyzing just a subset:

CALL system.drop_stats(schema_name, table_name, ARRAY[ARRAY['p2_value1', 'p2_value2']])

Statistics collection is supported for Hive Metastore and Amazon Glue.

Amazon Glue Support

Configuring and using Presto with AWS Glue is described in the AWS Glue Support documentation section.

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).

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.


CREATE TABLE hive.avro.avro_data (
   id bigint
   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.


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.


  • 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)

    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.

  • system.drop_stats(schema_name, table_name, partitions)

    Drops statistics for a subset of partitions or the entire table. The partitions are specified as an array whose elements are arrays of partition values (similar to the partition_values argument in create_empty_partition). A null value for the partition_values argument indicates that stats should be dropped for the entire table.


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'

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']);

Drop stats for a partition of the page_views table:

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

Query the page_views table:

SELECT * FROM hive.web.page_views

List the partitions of the page_views table:

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

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/'

Collect statistics for the request_logs table:

ANALYZE hive.web.request_logs;

The examples shown here should work on Google Cloud Storage after replacing s3:// with gs://.

Cleaning up

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.