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Realtime Compute for Apache Flink
Realtime Compute for Apache Flink
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Introduction
What is Alibaba Cloud Realtime Compute for Apache Flink?
Architecture of Realtime Compute for Apache Flink in exclusive mode
Service types
Scenarios
Fully Managed Flink
Announcements
Pricing
Overview
Overview
Introduction to the console of fully managed Flink
Upstream and downstream storage
Preparations
Assign a role to an account
Configure a whitelist
Grant permissions to an account
Procedure for activating the service
Manage projects
Configure a session cluster
Flink SQL Reference
Overview
Keywords
Create a data view
DDL statements
Create a source table
Create a Log Service source table
Create a Message Queue for Apache Kafka source table
Create a full MaxCompute source table
Create an incremental MaxCompute source table
Create a Message Queue for Apache RocketMQ source table
Create a Hologres source table
Create a full Elasticsearch source table
Create a PostgreSQL CDC source table (public preview)
Create a MySQL CDC source table (public preview)
Create an Upsert Kafka source table
Create a Datagen source table
Create a result table
Create a Log Service result table
Create a Message Queue for Apache Kafka result table
Create a Tablestore result table
Create a MaxCompute result table
Create a Message Queue for Apache RocketMQ result table
Create an ApsaraDB for HBase result table
Create an ApsaraDB for Redis result table
Create a Hologres result table
Create an ApsaraDB for MongoDB result table
Create an Elasticsearch result table
Create a Phoenix5 result table
Create an ApsaraDB RDS for MySQL result table
Create an AnalyticDB for MySQL V3.0 result table
Create a ClickHouse result table
Create a print result table
Create an Upsert Kafka result table
Create a blackhole result table
Create an InfluxDB result table
Create a dimension table
Create an ApsaraDB for HBase dimension table
Create a Tablestore dimension table
Create a MaxCompute dimension table
Create an ApsaraDB for Redis dimension table
Create an Elasticsearch dimension table
Create a Hologres dimension table
Create an ApsaraDB RDS for MySQL dimension table
Create an AnalyticDB for MySQL V3.0 dimension table
Create a FileSystem dimension table
INSERT INTO statement
DQL statements
Window functions
Overview
Tumbling windows
Sliding windows
Session windows
OVER windows
Built-in functions
Overview
Scalar functions
BITAND
BITNOT
BITOR
BITXOR
CONV
DATEDIFF
DATE_ADD
DATE_FORMAT
DATE_FORMAT_TZ
DATE_SUB
FROM_UNIXTIME
ISNAN
JSON_VALUE
KEYVALUE
MURMUR_HASH
MURMUR_HASH_64
PARSE_URL
REGEXP
REGEXP_REPLACE
SPLIT_INDEX
TO_DATE
TO_TIMESTAMP_TZ
UNIX_TIMESTAMP
Table-valued functions
GENERATE_SERIES
JSON_TUPLE
MULTI_KEYVALUE
STRING_SPLIT
Aggregate functions
APPROX_COUNT_DISTINCT
FIRST_VALUE
LAST_VALUE
CLUSTER_SERVING
User-defined functions
Java
Overview
UDFs
UDAFs
UDTFs
Python
Overview
UDFs
UDAFs
UDTFs
Flink SQL Developer Guide
Overview
Develop a job
Start a job
Suspend or terminate a job
Debug a job
Manage UDFs
Configure a Hive metastore
Manage custom connectors
Job optimization
Recommended Flink SQL practices
Configure resources in expert mode
Flink DataStream Developer Guide
Overview
Develop a job
Publish a job
Start a job
Suspend or terminate a job
Flink Python Developer Guide
Overview
Develop a job
Publish a job
Start a job
Suspend or terminate a job
Python dependencies
O&M management
Job O&M
Manage job versions
Configure alert rules (on the fully managed Flink tab)
Configure monitoring alerts (in the ARMS console)
Configure job logs
View job logs
Configure Autopilot
Cluster configuration management
Reconfigure workspace resources
Manage components
Modify a vSwitch
Security and monitoring
ActionTrail logs
SDK Reference
OpenAPI SDK
FAQ
Basic concepts
Reference
Troubleshooting
Upstream and downstream storage
Common SQL errors
Agreements
Flink Serverless SLA
Exclusive Mode
Announcements
Release notes
Blink-3.7.10
Blink 3.7.9
Blink-3.6.8
Blink-3.6.5
Blink 3.6.2
Blink-3.6.0
Blink-3.5.0-hotfix
Blink 3.4.4
Blink 3.4.3
Blink 3.3.0
Blink 3.2.3
Blink 3.2.1
Blink 3.2.1 release notes
API compatibility report: Blink 3.2 and Flink 1.5.1
Incompatible SQL items: Blink 3.0 and Blink 2.0
Product Introduction
Overview
Development history
Workflow
Upstream and downstream data stores
Product security
Limits
Pricing
Billable items
Billing methods
Specification selection
Renewal
Manual renewal
Auto-renewal
Change resource configurations
Preparation
Alibaba Cloud account and RAM user authorization
Activate Realtime Compute for Apache Flink and create a project
Role authorization
Assign a RAM role to an account that uses Realtime Compute for Apache Flink in exclusive mode
Flink SQL
Overview
Keywords
Basic concepts
Time zone
Time attributes
Watermark
Computed column
Data types
Data type conversion
Mathematical and logical operations between data types
Create a data view
DDL statements
Overview
Create a source table
Create a result table
Create a dimension table
DML statement
INSERT INTO statements
EMIT statements
Query statements
SELECT statements
WHERE statement
HAVING statement
GROUP BY statement
JOIN statements
JOIN statements for dimension tables
IntervalJoin statement
UNION ALL
TopN
GROUPING SETS clause
CEP statements
Deduplication statements
Window functions
Overview
Tumbling window
Sliding windows
Session window
OVER window
Built-in functions
String functions
Mathematical functions
Date functions
Logical functions
Conditional functions
Table-valued functions
Type conversion function
Aggregate functions
Other functions
UDX
Overview
UDF
UDAF
UDTF
Develop a UDX by using IntelliJ IDEA
Flink SQL Development Guide
Overview
Data storage
Overview
Data storage resource registration
Authorize Realtime Compute for Apache Flink to access a VPC
Configure a whitelist for accessing storage resources
Data development
Develop a job
Publish a job
Start a job
Suspend a job
Terminate a job
Job debugging
Local debugging
Online debugging
Data administration
Go to the Job Administration page
Running information
Curve charts
Timeline
Failover
Checkpoints
JobManager
TaskExecutor
Data lineage
Properties and parameters
Job diagnosis
Configuration optimization
Overview
Recommended Flink SQL practices
Performance optimization by using automatic configuration
Performance optimization by using auto scaling
Optimize performance by manual configuration
Typical backpressure scenarios and optimization ideas
SQL Tuning Advisor
Asynchronous mode
Cache policy
Local-global optimization
ROW_NUMBER OVER WINDOW
miniBatch and microBatch
Partial-final optimization
APPROX_COUNT_DISTINCT
Partitioned All Cache
Monitoring and alerting
Manage Blink versions of a Realtime Compute for Apache Flink cluster deployed in exclusive mode
Customize log levels and download paths
Flink Datastream Development Guide
Overview
Configure a whitelist for accessing storage resources
Set custom parameters
Monitoring and alerts
Develop a job
Publish a job
Example of DataStream jobs
Read data from DataHub
Read data from Message Queue for Apache Kafka
Read data from DataHub and write data to ApsaraDB for HBase
Read data from Log Service
Best Practices
Best practices of Realtime Compute in the e-commerce industry
Latest transaction records in e-commerce scenarios
Real-time situation awareness and geographic distribution of orders in e-commerce scenarios
Marketing coupons in e-commerce scenarios
Real-time PV and UV curves in e-commerce scenarios
Best practices of Realtime Compute in the IoT industry
Multidimensional analysis of data from IoT sensors
Best practices of Realtime Compute in the live streaming industry
Core video metric monitoring for live streaming
Digital operations for live streaming
Agreements
Realtime Compute for Apache Flink Service Level Agreement
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Realtime Compute for Apache Flink
Exclusive Mode
Flink SQL Development Guide
Data storage
Data storage resource registration
This Product
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Realtime Compute for Apache Flink
Register an AnalyticDB for MySQL instance
Register a Tablestore instance
Register an ApsaraDB for RDS instance
Register a Log Service project
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