Please keep the discussion on the mailing list rather than commenting on the wiki wiki discussions get unwieldy fast. As we have proposed in dev-list. We have developed a Pulsar Flink Connector based on Flink 1. Apache Pulsar is a multi-tenant, high-performance distributed pub-sub messaging system. Pulsar includes multiple features such as native support for multiple clusters in a Pulsar instance, with seamless geo-replication of messages across clusters, very low publish and end-to-end latency, seamless scalability to over a million topics, and guaranteed message delivery with persistent message storage provided by Apache BookKeeper.
Nowadays, Pulsar has been adopted by more and more companies. Source: The exactly-once source should implement CheckpointedFunction to persist reading status MessageId in Pulsar which uniquely identifies a message in topic to state store, and could notify Pulsar of messages finished consumption by implementing CheckpointListener.
Sink: The at-least-once sink should implement CheckpointedFunction to flush all messages written by sink tasks to Pulsar on snapshotState. A partitioned topic in Pulsar is internally implemented as multiple topics sharing one same prefix:.
Therefore, a topic partition is actually a topic in Pulsar and we could use it independently. All source tasks share the same logic on distributing partition among tasks. Therefore, each discoverer can identify whether it is responsible for one newly come partition and start a reader accordingly. Therefore, whenever a snapshot for a checkpoint is requested for a source task, the task checks all reader threads with its reading position and adds each topic-name, message ID pair to the state.
When recovering from failure, the reader threads seek the snapshotted message ID and re-consume all messages after it. By default, Pulsar brokers immediately delete all messages that have been acknowledged by a consumer. However, we cannot ack messages in reader thread immediately since the dataflow graph would fail, and we need to replay sources by message ID. In Flink, when a checkpoint is finished, it means all records that the source had emitted before the checkpoint went through the streaming dataflow and updated the corresponding operator states which are also snapshotted.
Whenever a checkpoint is finished, we move the cursor to checkpointed message IDs. As shown in the figure below, the durable cursor is moved forward once the checkpoint is completed. When you send a message to Pulsar using sendAsync, your message will be buffered in a pendingMessages queue, and you will get a CompletableFuturehandle.
You can register a callback on the handle and get notified on completion. Another Pulsar producer API flush sends all messages buffered in the client directly and wait until all messages have been successfully persisted. We use these two APIs in our Pulsar sink implementation to guarantee its at-least-once semantic.
For each record we receive in the sink, we send it to Pulsar with sendAsync and maintain a count pendingRecords that has not been persistent. On each checkpoint, we call flush manually and wait for message acknowledgments from Pulsar brokers. The checkpoint is considered complete when we get all acknowledgments and pendingRecordsdecreases to 0, and the checkpoint is regarded as a failure if an exception occurs while persisting messages.
By default, a failing checkpoint in Flink causes an exception that results in an application restart; therefore, messages are guaranteed to be persisted at least once. Each source task would fetch the schema through Pulsar Admin API independently, and share the schema with all reader threads that read data from Pulsar topics directly. The conversion are of two folds:. AVRO Foo. Therefore these parts are left unimplemented in Pulsar catalog.
The topic to be consumed. The topic list to be consumed.It supports all features of the HadoopOffice library, such as encryption, signing, linked workbooks, templates or low footprint mode. The Table Source is available for Scala 2. Example for the file testsimple. This file has dates as Excel standard in US format and decimals stored in German format e. It skips the first line of the first sheet in the Excel, because it is the header line and does not contain data.
The following example describes how a result from a query "testSimpleResult" of a FlinkTable is stored as a file in new Excel Format see mimetype in Hadoop File Format. It writes the field names of the Flink Table as the first row of the Excel useHeader: true. All data is written in the Sheet "Sheet 1". Skip to content. The order of the field commands describe which column of the Excel is meant, e. Note that in most cases - even for Excels written using Excel for Non-US countries you may want to choose US, because this is the Excel default format.
However, if your dates are Excel strings you may need to select other formats. Default: Locale. Those can vary from country to country. For instance, in Germany the comma is the equivalent to the US dot. Default: Locale of the system dateTimeFormat as of 1. You can define any Locale here to read timestamps. However using SimpleDateFormat you can define any date format pattern.
Default: null this means the pattern defined in java. Timestamp will be used. US decimalFormat: Format of decimals. Pages You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.Please keep the discussion on the mailing list rather than commenting on the wiki wiki discussions get unwieldy fast.
A partition is a division of a logical database or its constituent elements into distinct independent parts.
Flink 1.9 实战：使用 SQL 读取 Kafka 并写入 MySQL
Database partitioning is normally done for manageability, performance or availability reasons, or for load balancing. Partition is widely used in hive. Especially in the ETL domain, most tables have partition attributes, which allow users to continue processing. Partition is more convenient for data management, time partitioning and business partitioning are common.
Table partitioning means dividing table data into some parts based on the values of particular columns. Write: Flink does not require users to create partitions in advance, and partitions are created automatically during writing. Partition in traditional databases is very complex, and they support rich partitioning criteria, includes:.
Creating a table from a POJO and use table sink to output fail
In today's big data systems, partition mainly comes from hive. The partition in Hive is only similar to the concept of single value list partition in traditional databases. There is no need for support rich partitioning criteria at present. Spark support hive partitioned by when use Hive catalog, and it also introduced its partitioned by DDL too when use inMemory catalog. The two methods of use are mutually exclusive. If use mysql as catalog storage, the partition filter will push down to mysql query.
This is the most efficient pruning method, which has less pressure on catalog and client.
Databricks delta is a transaction storage layer specially designed to use Apache Spark and Databricks File System. It does partition pruning by launching a Spark SQL job. First, it reads checkpoint and changeLog, gets the current readable file list, and then filter it according to condition, and get the final partitions.
One of the main reasons is that partition pruning is too heavy in Delta. It needs to merge checkpoint and changeLog, and there may be many smaller files, so it needs to start a Spark SQL job to complete.
At present, the partition we want to support is similar to that of hive and only supports single value list partition. User can verify it by command:. Let the engine dynamically determine the partitions based on the values of the partition column from source table.The open source data technology frameworks Apache Flink and Apache Pulsar can integrate in different ways to provide elastic data processing at large scale.Robust Stream Processing with Apache Flink
I recently gave a talk at Flink Forward San Francisco and presented some of the integrations between the two frameworks for batch and streaming applications. In this post, I will give a short introduction to Apache Pulsar and its differentiating elements from other messaging systems and describe the ways that Pulsar and Flink can work together to provide a seamless developer experience for elastic data processing at scale. Apache Pulsar is an open-source distributed pub-sub messaging system under the stewardship of the Apache Software Foundation.
Pulsar is a multi-tenant, high-performance solution for server-to-server messaging including multiple features such as native support for multiple clusters in a Pulsar instance, with seamless geo-replication of messages across clusters, very low publish and end-to-end latency, seamless scalability to over a million topics, and guaranteed message delivery with persistent message storage provided by Apache BookKeeper among others.
The first differentiating factor stems from the fact that although Pulsar provides a flexible pub-sub messaging system it is also backed by durable log storage — hence combining both messaging and storage under one framework. Because of that layered architecture, Pulsar provides instant failure recovery, independent scalability and balance-free cluster expansion.
The second differentiator of Pulsar is that the framework is built from the get-go with multi-tenancy in mind. What that means is that each Pulsar topic has a hierarchical management structure making the allocation of resources as well as the resource management and coordination between teams efficient and easy. As shown in the below diagram, Pulsar holds the data in the topic while multiple teams can consume the data independently depending on their workloads and data consumption patterns.
Apache Flink is a streaming-first computation framework that perceives batch processing as a special case of streaming. Apache Pulsar has a similar perspective to that of Apache Flink with regards to the data layer. The framework also uses streams as a unified view on all data, while its layered architecture allows traditional pub-sub messaging for streaming workloads and continuous data processing or usage of Segmented Streams and bounded data stream for batch and static workloads.
With Pulsar, once a producer sends data to a topic, it is partitioned depending on the data traffic and then further segmented under those partitions — using Apache Bookkeeper as segment store — to allow for parallel data processing as illustrated in the diagram below. This allows a combination of traditional pub-sub messaging and distributed parallel computations in one framework. Apache Flink and Apache Pulsar integrate in multiple ways already.
In the following sections, I will present some potential future integrations between the frameworks and share examples of existing ways in which you can utilize the frameworks together.
Pulsar can integrate with Apache Flink in different ways. Some potential integrations include providing support for streaming workloads with the use of Streaming Connectors and support for batch workloads with the use of Batch Source Connectors.
Pulsar also comes with native support for schema that can integrate with Flink and provide structured access to the data, for example by using Flink SQL as a way of querying data in Pulsar. Finally, an alternative way of integrating the technologies could include using Pulsar as a state backend with Flink.
Since Pulsar has a layered architecture Streams and Segmented Streamspowered by Apache Bookkeeperit becomes natural to use Pulsar as a storage layer and store Flink state. From an architecture point of view, we can imagine the integration between the two frameworks as one that uses Apache Pulsar for a unified view of the data layer and Apache Flink as a unified computation and data processing framework and API.
Integration between the two frameworks is ongoing and developers can already use Pulsar with Flink in multiple ways. For example, Pulsar can be used as a streaming source and streaming sink in Flink DataStream applications. Developers can ingest data from Pulsar into a Flink job that makes computations and processes real-time data, to then send the data back to a Pulsar topic as a streaming sink.
Such an example is shown below:. Another integration between the two frameworks that developers can take advantage of includes using Pulsar as both a streaming source and a streaming table sink for Flink SQL or Table API queries as shown in the example below:.
Finally, Flink integrates with Pulsar for batch workloads as a batch sink where all results get pushed to Pulsar after Apache Flink has completed the computation in a static data set. Subscribe to the Apache Flink and Apache Pulsar mailing lists to stay up-to-date with the latest developments in this space or share your thoughts and recommendations with both communities.
All Rights Reserved. What is Apache Flink?Today, we are announcing the release of Stateful Functions StateFun 2. This release marks a big milestone: Stateful Functions 2. In this blog post, you will learn our motivation behind the Flink-Hive integration, and how Flink 1.
All Rights Reserved. What is Apache Flink? What is Stateful Functions? All streaming use cases. Guaranteed correctness. Exactly-once state consistency Event-time processing Sophisticated late data handling Learn more. Layered APIs. Operational Focus. Flexible deployment High-availability setup Savepoints Learn more.
Scales to any use case. Scale-out architecture Support for very large state Incremental checkpointing Learn more. Excellent Performance. Low latency High throughput In-Memory computing Learn more. Powered by Flink. Flink Serialization Tuning Vol. Stateful Functions 2. Flink Community Update - April'20 While things slow down around us, the Apache Flink community is privileged to remain as active as ever.
This blogpost combs through the past few months to give you an update on the state of things in Flink — from core releases to Stateful Functions; from some good old community stats to a new development blog.The following diagram still represents the levels of abstraction Flink exposes. Check out the documentation on the specifics.
The release notes are also a must-read. We will focus on the Table API changes, but there are also many other changes that are huge steps forward — especially regarding state improvement and containerization support.
These changes constitute an entirely new interface for the Table API and make the entire model much easier to use easily and effectively. As an example, we will start with a simple python producer in Kafka. We create messages for temperature for each IoT device, in this case, a fictitious brewery.
We will produce the data in three-second intervals into a Kafka topic the sourcethen roll up the data, performing a simple computation by a one minute window, and emit the data back to a Kafka topic the sink.
The Flink job using the Table API handles the reads from the source, the computation, and the write to the sink. Our source data will be JSON, and so will our aggregated output data. For this example, we will be using the Eventador Stack — so all the steps are fairly Eventador specific, but you can also change things to run this example locally.
Flink TableSource TableSink
We need to create two topics, one for the source and one for the sink. Check out the Eventador documentation for a step-by-step guide. This data generator utilizes the Python Kafka driver to produce data to a Kafka cluster.
The generator code is available as a Gistyou can download it from Github and run it. This can be run on the command line to generate data continuously. Be sure to change the connect string to the connect string exposed in the Eventador Console application. Executing the program and the resulting output should look like this:.
Let this run for a while, or better yet, background it and let it continue to produce data to Kafka. We want to aggregate data group by in SQL by a time bucket, and then write the result out to Kafka. The Flink job is available on Github here. It can be run inside the Eventador Console directly from Github, no need to deal with Maven at all.
The job is then submitted to run on a Flink cluster. You will need to close the example repo then specify it as a new Project in Eventador. Choose the option to import a project into Eventador.
Once a project is created we can choose to run it on our Flink cluster. We use the Flink Kafka connector to connect to Kafka and consume data. The API has been updated and is very straightforward to set properties including bootstrap.
You can see all the options available in the docs here. We use.
The Flink Table And SQL API With Apache Flink 1.6
There are properties that can be set that control what offset to use to start reading from the Kafka topic —. Timestamps are of particular importance when reading data from Kafka. In this example, we create a virtual column and populate it via.
Alternatively, using a time value from the data is possible via. For more information on setting timestamps from Kafka, check out the docs here. Registering a table as a sink is very similar to registering a table as a source. In this case, we are doing a group by and outputting a key sensoran aggregate column avgTemp and a couple of timestamps hopStart and hopEnd. When using Kafka, it expects a partitioning strategy.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. So separate source translation if DML insert, this a workaround since we have no conclusion for the output type definition of sink operation compare to traditional SQL insert.
Thanks for the PR and sorry for the long time to review this. I think this is a very nice feature and should be supported. However, I think we have to change the TableSink interface for that. In the current state, a TableSink is configured to emit data according to the query result, i. What do you think lincoln-lil?
I'm not sure if we should add this functionality to the sql method. Returning null is not a good idea in my opinion. I would rather add a new method maybe called sqlInsert. The method would check the query type and could also give a meaningful error message with a hint of which method should be used instead.
This will return the default configuration. We could also implement a TableSink which stores results in a static List guarded by a lock to synchronize this inserts. This would make the test faster and more reliable going of the the filesystem is a reason for flaky tests. The test base starts a Flink system which make the test quite expensive. This test will fail before the Flink system can be used.
I think we should use a TableSink which is backed by a static List object for tests because it is faster and more reliable. I read the design doc again, the original idea to support this functionality was doing minimal changes in the current state. Yes, currently a TableSink's schema is always derived from the query result, and before it is configured the schema is null. Practically, I found that we can overwrite the getOutputType method to declare a concrete sink table's schema, and this can be used in a dml sql validation.
So without adding an new SinkTable or modifying TableSink interface, we can choose keeping writeToSink method in TableAPI as a usage of derive table and tell users they can implement a custom type TableSink with output type declaration as a predefined schema.