- Release Notes and Announcements
- Product Introduction
- Purchase Guide
- Getting Started
- EMR on CVM Operation Guide
- Planning Cluster
- Configuring Cluster
- Managing Cluster
- Instance Information
- Node Specification Management
- Checking and Updating Public IP
- Logging in to Clusters
- Cluster Scale-Out
- Cluster Scale-in
- Releasing Now
- Auto Scaling
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- Changing Configurations
- Automatic Replacement
- Exporting Software Configuration
- Cluster Scripts
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- Task Center
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- Cluster Overview
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- Service Status
- Cluster Event
- Log
- Application Analysis
- Cluster Inspection
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- Node Monitoring Metrics
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- Storm Monitoring Metrics
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- Kafka Monitoring Metrics
- Alarm Configurations
- Alarm Records
- EMR on TKE Operation Guide
- EMR Serverless HBase Operation Guide
- EMR Development Guide
- Hadoop Development Guide
- HDFS Common Operations
- HDFS Federation Management Development Guide
- HDFS Federation Management
- Submitting MapReduce Tasks
- Automatically Adding Task Nodes Without Assigning ApplicationMasters
- YARN Task Queue Management
- Practices on YARN Label Scheduling
- Hadoop Practical Tutorial
- Using API to Analyze Data in HDFS and COS
- Dumping YARN Job Logs to COS
- Spark Development Guide
- Hbase Development Guide
- Phoenix on Hbase Development Guide
- Hive Development Guide
- Presto Development Guide
- Sqoop Development Guide
- Hue Development Guide
- Oozie Development Guide
- Flume Development Guide
- Kerberos Development Guide
- Knox Development Guide
- Alluxio Development Guide
- Kylin Development Guide
- Livy Development Guide
- Kyuubi Development Guide
- Zeppelin Development Guide
- Hudi Development Guide
- Superset Development Guide
- Impala Development Guide
- Druid Development Guide
- TensorFlow Development Guide
- Kudu Development Guide
- Ranger Development Guide
- Kafka Development Guide
- Iceberg Development Guide
- StarRocks Development Guide
- Flink Development Guide
- Hadoop Development Guide
- Practical Tutorial
- API Documentation
- FAQs
- Service Level Agreement
- Contact Us
- Release Notes and Announcements
- Product Introduction
- Purchase Guide
- Getting Started
- EMR on CVM Operation Guide
- Planning Cluster
- Configuring Cluster
- Managing Cluster
- Instance Information
- Node Specification Management
- Checking and Updating Public IP
- Logging in to Clusters
- Cluster Scale-Out
- Cluster Scale-in
- Releasing Now
- Auto Scaling
- Graceful Scale-In
- Changing Configurations
- Automatic Replacement
- Exporting Software Configuration
- Cluster Scripts
- Cluster Termination
- Operation Logs
- Task Center
- Disk Management
- Managing Service
- Monitoring and Alarms
- Cluster Overview
- Node Status
- Service Status
- Cluster Event
- Log
- Application Analysis
- Cluster Inspection
- Java Analysis
- Monitoring Metrics
- Node Monitoring Metrics
- HDFS Monitoring Metrics
- YARN Monitoring Metrics
- ZooKeeper Monitoring Metrics
- HBase Monitoring Metrics
- Hive Monitoring Metrics
- Spark Monitoring Metrics
- Presto Monitoring Metrics
- Trino Monitoring Metrics
- ClickHouse Monitoring Metrics
- Druid Monitoring Metrics
- Kudu Monitoring Metrics
- Alluxio Monitoring Metrics
- PrestoSQL Monitoring Metrics
- Impala Monitoring Metrics
- Ranger Monitoring Metrics
- COSRanger Monitoring Metrics
- Doris Monitoring Metrics
- Kylin Monitoring Metrics
- Zeppelin Monitoring Metrics
- Oozie Monitoring Metrics
- Storm Monitoring Metrics
- Livy Monitoring Metrics
- Kyuubi Monitoring Metrics
- StarRocks Monitoring Metrics
- Kafka Monitoring Metrics
- Alarm Configurations
- Alarm Records
- EMR on TKE Operation Guide
- EMR Serverless HBase Operation Guide
- EMR Development Guide
- Hadoop Development Guide
- HDFS Common Operations
- HDFS Federation Management Development Guide
- HDFS Federation Management
- Submitting MapReduce Tasks
- Automatically Adding Task Nodes Without Assigning ApplicationMasters
- YARN Task Queue Management
- Practices on YARN Label Scheduling
- Hadoop Practical Tutorial
- Using API to Analyze Data in HDFS and COS
- Dumping YARN Job Logs to COS
- Spark Development Guide
- Hbase Development Guide
- Phoenix on Hbase Development Guide
- Hive Development Guide
- Presto Development Guide
- Sqoop Development Guide
- Hue Development Guide
- Oozie Development Guide
- Flume Development Guide
- Kerberos Development Guide
- Knox Development Guide
- Alluxio Development Guide
- Kylin Development Guide
- Livy Development Guide
- Kyuubi Development Guide
- Zeppelin Development Guide
- Hudi Development Guide
- Superset Development Guide
- Impala Development Guide
- Druid Development Guide
- TensorFlow Development Guide
- Kudu Development Guide
- Ranger Development Guide
- Kafka Development Guide
- Iceberg Development Guide
- StarRocks Development Guide
- Flink Development Guide
- Hadoop Development Guide
- Practical Tutorial
- API Documentation
- FAQs
- Service Level Agreement
- Contact Us
Using Phoenix Salted Table
HBase sequential write may suffer from region server hotspotting if your row key is monotonically increasing. Phoenix provides a way to transparently salt the row key with a salting byte for a particular table. You need to specify this in table creation time by specifying a table property "SALT_BUCKETS" with a value from 1 to 256. Like this:
0: jdbc:phoenix:>CREATE TABLE table (a_key VARCHAR PRIMARY KEY, a_col VARCHAR) SALT_BUCKETS = 20;
Salting the row key provides a way to mitigate the problem caused by HBase sequential write. With salted tables, you don't need to be knowledgeable about the row key design in HBase. Below is Phoenix's official performance comparison of read and write performance between salted and non-salted tables:
Phoenix Secondary Indexing
Secondary indexes are an orthogonal way to access data from its primary access path. In HBase, you have a single index that is lexicographically sorted on the primary row key. Access to records in any way other than through the primary row requires scanning over potentially all the rows in the table to test them against your filter. With secondary indexing, the columns or expressions you index form an alternate row key to allow point lookups and range scans along this new axis.
Configuring Secondary Indexing in Phoenix
Phoenix on EMR supports Phoenix's secondary indexing. If you need to use non-transactional, variable indexing, just follow the steps below to configure it. Go to the component management page in the EMR console, click HBase, select Configuration > Configuration Management, and add three configuration items in hbase-site.xml:
hbase.regionserver.wal.codec, hbase.region.server.rpc.scheduler.factory.class, and hbase.rpc.controllerfactory.class. The detailed configuration is as follows:<property><name>hbase.regionserver.wal.codec</name><value>org.apache.hadoop.hbase.regionserver.wal.IndexedWALEditCodec</value></property><property><name>hbase.region.server.rpc.scheduler.factory.class</name><value>org.apache.hadoop.hbase.ipc.PhoenixRpcSchedulerFactory</value><description>Factory to create the Phoenix RPC Scheduler that uses separate queues for index and metadata updates</description></property><property><name>hbase.rpc.controllerfactory.class</name><value>org.apache.hadoop.hbase.ipc.controller.ServerRpcControllerFactory</value><description>Factory to create the Phoenix RPC Scheduler that uses separate queues for index and metadata updates</description></property>
Using Secondary Indexing in Phoenix
To create a secondary index, run the following command:
0: jdbc:phoenix:>CREATE INDEX my_index ON my_table (v1) INCLUDE (v2);
For more information on secondary indexing, please see the community documentation of Phoenix secondary indexing.
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