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.

openLooKeng 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

In order to enable first-class support for Avro tables and CSV files when using Hive 3.x, you need to add the following property definition to the Hive metastore configuration file hive-site.xml:

<property>
     <!-- https://community.hortonworks.com/content/supportkb/247055/errorjavalangunsupportedoperationexception-storage.html -->
     <name>metastore.storage.schema.reader.impl</name>
     <value>org.apache.hadoop.hive.metastore.SerDeStorageSchemaReader</value>
 </property>

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, openLooKeng will create acatalog named sales using the configured connector.

HDFS Configuration

For basic setups, openLooKeng 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 openLooKeng nodes. If you are referencing existing Hadoop config files, make sure to copy them to any openLooKeng nodes that are not running Hadoop.

HDFS Username and Permissions

Before running any CREATE TABLE or CREATE TABLE AS statements for Hive tables in openLooKeng, you need to check that the user openLooKeng is using to access HDFS has access to the Hive warehouse directory. 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.

When not using Kerberos with HDFS, openLooKeng will access HDFS using the OS user of the openLooKeng process. For example, if openLooKeng 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 openLooKeng JVM Config, replacing hdfs_userwith the appropriate username:

-DHADOOP_USER_NAME=hdfs_user

The hive user generally works, since Hive is often started with the hive user and this user has access to the Hive warehouse.

Whenever you change the user openLooKeng is using to access HDFS, remove /tmp/openlookeng-*,/tmp/presto-*,/tmp/hetu-* on HDFS, as the new user may not have access to the existing temporary directories.

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 section for a more detailed discussion of the security options in the Hive connector.

Hive Configuration Properties

Hive Thrift Metastore Configuration 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.

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

S3 Configuration Properties

S3 Credentials

If you are running openLooKeng 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

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

S3 Data Encryption

openLooKeng 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 openLooKeng 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, openLooKeng 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 openLooKeng 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

openLooKeng 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-gcs-tutorial for step-by-step instructions.

GCS Configuration properties

GCS Configuration properties

ORC Cache Configuration

Hive connector caches the ORC file data to provide better performance. The data is cached in worker local memory. Support for caching partitioned table is only available. cache sql can be used to customize the table and partition that should be cached by the Connector.

ORC Cache Properties

Table Statistics

When writing data, the Hive connector always collects basic statistics (numFiles, numRows, rawDataSize, totalSize) and by default will also collect column level statistics:

Updating table and partition statistics

If your queries are complex and include joining large data sets, running /sql/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.

Hive ACID support

openLooKeng supports ACID transactions (INSERT,UPDATE,DELETE) on a transactional table in Hive.

Create Transactional Table using Hive Connector

To support ACID transactions, following prerequisites must be met:

1. Table should be created with ‘transactional’ property enabled.
2. Format as ORC. Refer #Limitations

Example:

CREATE TABLE hive_acid_table (
    id int,
    name string )
  WITH (format='ORC', transactional=true);

INSERT on transactional tables

Insert operation remains same from end-user perspective for both transactional and non-transactional tables.

Example:

INSERT INTO hive_acid_table
  VALUES
     (1, 'foo'),
     (2, 'bar');

UPDATE on transactional tables

UPDATE operation on transactional table lets user update specific rows’ columns matching with WHERE clause.

Example:

UPDATE hive_acid_table
  SET name='john'
  WHERE id=2;

Above example updates the column name‘s value to john of row with column id having value 2.

SELECT result before UPDATE:

lk:default> SELECT * FROM hive_acid_table;
id | name
----+------
  2 | bar
  1 | foo
(2 rows)

SELECT result after UPDATE

lk:default> SELECT * FROM hive_acid_table;
 id | name
----+------
  2 | john
  1 | foo
(2 rows)

DELETE on transactional tables

DELETE operation on transactional tables lets user delete specific rows matching with WHERE clause.

Example:

DELETE FROM hive_acid_table
  WHERE id=2;

Above example delete the row with column id having value 2.

SELECT result before DELETE:

lk:default> SELECT * FROM hive_acid_table;
 id | name
----+------
  2 | john
  1 | foo
(2 rows)

SELECT result after DELETE:

lk:default> SELECT * FROM hive_acid_table;
 id | name
----+------
  1 | foo
(1 row)

VACUUM on transactional tables

Hive maintains all transactions (INSERT/UPDATE/DELETE) in separate delta directories for bookkeeping purposes. DELETE transactions does not remove the old rows physically in stored data, instead they just mark them as deleted in a new file. UPDATE uses split-update mechanism to update data. Due to these, read operations on table needs to read many files, which adds extra overhead. All these delta files needs to be merged to get consolidated data for faster processing. VACUUM operation in openLooKeng does the work of merging these delta files.

Vacuum operation translates to compaction in Hive. In Hive there are two types of compactions, Major and Minor.

Following are mapping between VACUUM and Hive compactions.

• VACUUM FULL’ translates to Major compaction.

• VACUUM’ translates to Minor compaction.

VACUUM

VACUUM TABLE hive_acid_table;

The above operation triggers the VACUUM on hive_acid_table to merge all delta directories to single delta directory. Once operation reaches RUNNING state, it continues to run asynchronously, unblocking client.

NOTE: Currently there is no command to fetch the result of asynchronous VACUUM, but it can be monitored from UI.

VACUUM FULL

Full Vacuum merges the all transactions present in delta directories and creates a base directory containing only final result. After this operation, deleted rows will be deleted permanently. As a result, during read, total data read will be very less, hence improves performance.

VACUUM TABLE hive_acid_table
  FULL;

The Above operation triggers the VACUUM FULL on hive_acid_table and once it reaches RUNNING state, it continues to run asynchronously, unblocking client.

VACUUM on partitioned table

If the table is partitioned then VACUUM operation can operate upon the specific partition separately instead of all partitions of the table together.

Example: Creating the partition table and INSERT data:

CREATE TABLE hive_acid_table_partitioned (
    id int,
    name string,
    class int) 
WITH (format='ORC', transactional=true, partitioned_by=ARRAY['class']);
INSERT INTO hive_acid_table_partitioned
  VALUES
    (1, 'foo', 5),
    (2, 'bar', 10);

Vacuum on specific partition class=5 can be run as:

VACUUM TABLE hive_acid_table_partitioned
   FULL
   PARTITION 'class=5';

If PARTITION 'class=5' is not specified then vacuum operation will run on all partitions and if any failure in any one partition fails the entire operation.

AND WAIT option

By default, VACUUM operations runs in asynchronously, i.e. Once the query reaches RUNNING state, client will not wait anymore for completion of operation.

Adding AND WAIT option to vacuum operation makes the client wait synchronously for the completion of Vacuum operation.

Above examples with AND WAIT options looks like below:

VACUUM TABLE hive_acid_table
  FULL
  AND WAIT;

VACUUM TABLE hive_acid_table
  AND WAIT;

VACUUM TABLE hive_acid_table_partitioned
  FULL
  PARTITION 'class=5'
  AND WAIT;

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

openLooKeng 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 openLooKeng 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 openLooKeng coordinator/worker nodes.

The table created in openLooKeng 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 openLooKeng 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

• 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.

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 openLooKeng.

Create a new Hive schema named web that will store tables in an S3bucket named my-bucket:

CREATE SCHEMA hive.web
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):

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']);

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
)
WITH (
  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 when table is non-transactional. for transactional table, the WHERE clause can be any condition.

• alter-schema usage fails, since the Hive metastore does not support renaming schemas.

• openLooKeng supports ACID transactions on Hive tables only for Hive version 3.x

• openLooKeng currently supports only ORC format for transactional tables.