kafka-connect-mq-sink | repository contains a Kafka Connect sink connector | Stream Processing library

 by   ibm-messaging Java Version: v1.3.1 License: Apache-2.0

kandi X-RAY | kafka-connect-mq-sink Summary

kandi X-RAY | kafka-connect-mq-sink Summary

kafka-connect-mq-sink is a Java library typically used in Data Processing, Stream Processing, Kafka applications. kafka-connect-mq-sink has build file available, it has a Permissive License and it has low support. However kafka-connect-mq-sink has 1 bugs and it has 1 vulnerabilities. You can download it from GitHub.

This repository contains a Kafka Connect sink connector for copying data from Apache Kafka into IBM MQ.

            kandi-support Support

              kafka-connect-mq-sink has a low active ecosystem.
              It has 27 star(s) with 34 fork(s). There are 12 watchers for this library.
              It had no major release in the last 12 months.
              There are 5 open issues and 24 have been closed. On average issues are closed in 28 days. There are 3 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of kafka-connect-mq-sink is v1.3.1

            kandi-Quality Quality

              kafka-connect-mq-sink has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 39 code smells.

            kandi-Security Security

              kafka-connect-mq-sink has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              kafka-connect-mq-sink code analysis shows 1 unresolved vulnerabilities (0 blocker, 1 critical, 0 major, 0 minor).
              There are 0 security hotspots that need review.

            kandi-License License

              kafka-connect-mq-sink is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              kafka-connect-mq-sink releases are available to install and integrate.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              kafka-connect-mq-sink saves you 453 person hours of effort in developing the same functionality from scratch.
              It has 1071 lines of code, 36 functions and 11 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed kafka-connect-mq-sink and discovered the below as its top functions. This is intended to give you an instant insight into kafka-connect-mq-sink implemented functionality, and help decide if they suit your requirements.
            • Benchmarks authentication of a registered queue manager
            • Gets all messages from queue
            • Builds a test message without a schema
            • Creates byte message
            • Build the struct message
            • Generate complex object as struct
            • Build a map message
            • Method to generate the complex object as a Map
            • This method is used to verify the JMS messages
            • Start the Connector
            • Define the configuration for this connector
            • Build a test message
            • Verifies that a message builder key header value is not supported
            • Verifies that the reply - queue name is a reply - queue URI
            • Generates a set of configuration configurations
            • Verifies that the MQ message builder key header value is unsupported
            • Configure this sink
            • Verify the topic
            • Verify reply queue property
            • Verifies the offset property
            • Test to see if we can connect to MQ
            • Gets the JMS message
            • Test to see if a JSON message is connected to MQ
            • Verify topic partition property
            Get all kandi verified functions for this library.

            kafka-connect-mq-sink Key Features

            No Key Features are available at this moment for kafka-connect-mq-sink.

            kafka-connect-mq-sink Examples and Code Snippets

            Externalizing secrets
            Javadot img1Lines of Code : 6dot img1License : Permissive (Apache-2.0)
            copy iconCopy
            # Additional properties for the worker configuration to enable use of ConfigProviders
            # multiple comma-separated provider types can be specified here
            Kafka Connect sink connector for IBM MQ,Data formats,The gory detail
            Javadot img2Lines of Code : 3dot img2License : Permissive (Apache-2.0)
            copy iconCopy
            Kafka Connect sink connector for IBM MQ,Running with Docker
            Javadot img3Lines of Code : 3dot img3License : Permissive (Apache-2.0)
            copy iconCopy
            docker run -v $(pwd)/config:/opt/kafka/config -p 8083:8083 kafkaconnect-with-mq-sink:1.3.0
            curl -X POST -H "Content-Type: application/json" http://localhost:8083/connectors \
              --data "@./config/mq-sink.json"

            Community Discussions


            How to do stream processing with Redpanda?
            Asked 2022-Mar-28 at 16:19

            Redpanda seems easy to work with, but how would one process streams in real-time?

            We have a few thousand IoT devices that send us data every second. We would like to get the running average of the data from the last hour for each of the devices. Can the built-in WebAssembly stuff be used for this, or do we need something like Materialize?



            Answered 2022-Mar-28 at 16:19

            Any Kafka library should work with RedPanda, including Kafka Streams, KSQL, Apache Spark, Flink, Storm, etc.

            Source https://stackoverflow.com/questions/71649313


            How to inject delay between the window and sink operator?
            Asked 2022-Mar-08 at 07:37
            Context - Application

            We have an Apache Flink application which processes events

            • The application uses event time characteristics
            • The application shards (keyBy) events based on the sessionId field
            • The application has windowing with 1 minute tumbling window
              • The windowing is specified by a reduce and a process functions
              • So, for each session we will have 1 computed record
            • The application emits the data into a Postgres sink
            Context - Infrastructure


            • It is hosted in AWS via Kinesis Data Analytics (KDA)
            • It is running in 5 different regions
            • The exact same code is running in each region


            • It is hosted in AWS via RDS (currently it is a PostgreSQL)
            • It is located in one region (with a read replica in a different region)

            Because we are using event time characteristics with 1 minute tumbling window all regions' sink emit their records nearly at the same time.

            What we want to achieve is to add artificial delay between window and sink operators to postpone sink emition.

            Flink App Offset Window 1 Sink 1st run Window 2 Sink 2nd run #1 0 60 60 120 120 #2 12 60 72 120 132 #3 24 60 84 120 144 #4 36 60 96 120 156 #5 48 60 108 120 168 Not working work-around

            We have thought that we can add some sleep to evictor's evictBefore like this



            Answered 2022-Mar-07 at 16:03

            You could use TumblingEventTimeWindows of(Time size, Time offset, WindowStagger windowStagger) with WindowStagger.RANDOM.

            See https://nightlies.apache.org/flink/flink-docs-stable/api/java/org/apache/flink/streaming/api/windowing/assigners/WindowStagger.html for documentation.

            Source https://stackoverflow.com/questions/71380016


            Why does my flink window trigger when I have set watermark to be a high number?
            Asked 2021-Jul-25 at 04:48

            I would expect windows to trigger only after we wait until the maximum possible time as defined by the max lateness for watermark.

            .assignTimestampsAndWatermarks( WatermarkStrategy.forBoundedOutOfOrderness(Duration.ofMillis(10000000)) .withTimestampAssigner((order, timestamp) -> order.getQuoteDatetime().getTime())) .keyBy(order-> GroupingsKey.builder().symbol(order.getSymbol()).expiration(order.getExpiration()) .build()) .window(EventTimeSessionWindows.withGap(Time.milliseconds(100000000)))

            In this example, why would the window ever trigger in any meaningful amount of time? The window is a very large window and we wait a very long time for records. When I run my example, the window still gets triggered in under a minute. why is that?



            Answered 2021-Jul-25 at 04:48

            Turns out the watermark was being generated after the source was exhausted(in this case it was from reading a file). So the max watermark was emitted(9223372036854775807). A trigger happens when: window.maxTimestamp() <= ctx.getCurrentWatermark()

            See https://stackoverflow.com/a/51554273/1099123

            Source https://stackoverflow.com/questions/68515397


            Why does this explicit definition of a storm stream not work, while the implicit one does?
            Asked 2021-May-28 at 09:57

            Given a simple Apache Storm Topology that makes use of the Stream API, there are two ways of initializing an Stream:

            Version 1 - implicit declaration



            Answered 2021-May-28 at 09:47

            That's because integerStream.filter(x -> x > 5); returns a new stream that you ignore.

            This works:

            Source https://stackoverflow.com/questions/67736466


            Apache Flink : filtering based on previous value
            Asked 2021-Apr-30 at 14:01

            All filtering examples in apache flink documentation display simple cases of filtering according to a global threshold.

            But what if filtering on an entry should take into account the previous entry?

            Let's say we have a stream of sensor data. We need to discard the current sensor data entry if it's X% larger than then previous entry.

            Is there a simple solution for this? Either in Apache Flink or in plain Java.




            Answered 2021-Apr-30 at 08:38

            In flink, this can be done with state.

            Your use case is very similar to the fraud detection example from flink doc.

            Source https://stackoverflow.com/questions/67330635


            How to expire keyed state with TTL in Apache Flink?
            Asked 2021-Apr-26 at 12:38

            I have a pipeline like this:



            Answered 2021-Apr-26 at 12:38

            The pipeline you've described doesn't use any keyed state that would benefit from setting state TTL. The only keyed state in your pipeline is the contents of the session windows, and that state is being purged as soon as possible -- as the sessions close. (Furthermore, since you are using a reduce function, that state consists of just one value per key.)

            For the most part, expiring state is only relevant for state you explicitly create, in which case you will have ready access to the state descriptor and can configure it to use State TTL. Flink SQL does create state on your behalf that might not automatically expire, in which case you will need to use Idle State Retention Time to configure it. The CEP library also creates state on your behalf, and in this case you should ensure that your patterns either eventually match or timeout.

            Source https://stackoverflow.com/questions/67259447


            How to set the publish interval for topology metrics in Apache Storm?
            Asked 2021-Apr-26 at 12:06

            While Apache Storm offers several metric types, I am interested in the Topology Metrics, (and not the Cluster Metrics or the Metrics v2. For these, a consumer has to be registered, for example as:



            Answered 2021-Apr-26 at 12:06

            After looking at the right place, I found the related configuration: topology.builtin.metrics.bucket.size.secs: 10 is they way to specify that interval in storm.yaml.

            Source https://stackoverflow.com/questions/67218020


            How to force Apache Flink using a modified operator placement?
            Asked 2021-Mar-24 at 10:00

            Apache Flink is distributes its operators on available, free slots on the JobManagers (Slaves). As stated in the documentation, there is the possibility to set the SlotSharingGroup for every operator contained in an execution. This means, that two operators can share the same slot, where they are executed later.

            Unfortunately, this option only allows to share the same group but not to assign a streaming operation to a specific slot.

            So my question is: What would be the best (or at least one) way to manually assign streaming operators to specific slots/workers in Apache Flink?



            Answered 2021-Mar-17 at 17:08

            You could disable the chaining via (disableChaining()) and start a new chain to isolate it from others via (startNewChain()). You can play with Flink Plan Visualizer to see if your plan has isolated operators. These modifiers applied affter the operator. Example:

            Source https://stackoverflow.com/questions/66641174


            What are stream-processing and Kafka-streams in layman terms?
            Asked 2021-Feb-05 at 11:30

            To understand what is kafka-streams I should know what is stream-processing. When I start reading about them online I am not able to grasp an overall picture, because it is a never ending tree of links to new concepts.
            Can any one explain what is stream-processing with a simple real-world example?
            And how to relate it to kafka-streams with producer consumer architecture?

            Thank you.



            Answered 2021-Feb-05 at 10:38
            Stream Processing

            Stream Processing is based on the fundamental concept of unbounded streams of events (in contrast to static sets of bounded data as we typically find in relational databases).

            Taking that unbounded stream of events, we often want to do something with it. An unbounded stream of events could be temperature readings from a sensor, network data from a router, order from an e-commerce system, and so on.

            Let's imagine we want to take this unbounded stream of events, perhaps its manufacturing events from a factory about 'widgets' being manufactured.

            We want to filter that stream based on a characteristic of the 'widget', and if it's red route it to another stream. Maybe that stream we'll use for reporting, or driving another application that needs to respond to only red widgets events:

            This, in a rather crude nutshell, is stream processing. Stream processing is used to do things like:

            • filter streams
            • aggregate (for example, the sum of a field over a period of time, or a count of events in a given window)
            • enrichment (deriving values within a stream of a events, or joining out to another stream)

            As you mentioned, there are a large number of articles about this; without wanting to give you yet another link to follow, I would recommend this one.

            Kafka Streams

            Kafka Streams a stream processing library, provided as part of Apache Kafka. You use it in your Java applications to do stream processing.

            In the context of the above example it looks like this:

            Kafka Streams is built on top of the Kafka producer/consumer API, and abstracts away some of the low-level complexities. You can learn more about it in the documentation.

            Source https://stackoverflow.com/questions/66058929


            How do I handle out-of-order events with Apache flink?
            Asked 2021-Feb-01 at 10:35

            To test out stream processing and Flink, I have given myself a seemingly simple problem. My Data stream consists of x and y coordinates for a particle along with time t at which the position was recorded. My objective is to annotate this data with velocity of the particular particle. So the stream might look some thing like this.



            Answered 2021-Jan-31 at 17:07

            One way of doing this in Flink might be to use a KeyedProcessFunction, i.e. a function that can:

            • process each event in your stream
            • maintain some state
            • trigger some logic with a timer based on event time

            So it would go something like this:

            • you need to know some kind of "max out of orderness" about your data. Based on your description, let's assume 100ms for example, such that when processing data at timestamp 1612103771212 you decide to consider you're sure to have received all data until 1612103771112.
            • your first step is to keyBy() your stream, keying by particle id. This means that the logic of next operators in your Flink application can now be expressed in terms of a sequence of events of just one particle, and each particle is processed in this manner in parallel.

            Something like this:

            Source https://stackoverflow.com/questions/65980505

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install kafka-connect-mq-sink

            To enable use of TLS, set the configuration mq.ssl.cipher.suite to the name of the cipher suite which matches the CipherSpec in the SSLCIPH attribute of the MQ server-connection channel. Use the table of supported cipher suites for MQ 9.1 here as a reference. Note that the names of the CipherSpecs as used in the MQ configuration are not necessarily the same as the cipher suite names that the connector uses. The connector uses the JMS interface so it follows the Java conventions. You will need to put the public part of the queue manager's certificate in the JSSE truststore used by the Kafka Connect worker that you're using to run the connector. If you need to specify extra arguments to the worker's JVM, you can use the EXTRA_ARGS environment variable.
            You will need to put the public part of the client's certificate in the queue manager's key repository. You will also need to configure the worker's JVM with the location and password for the keystore containing the client's certificate. Alternatively, you can configure a separate keystore and truststore for the connector.


            By default, the connector does not use the keys for the Kafka messages it reads. It can be configured to set the JMS correlation ID using the key of the Kafka records. To configure this behavior, set the mq.message.builder.key.header configuration value. In MQ, the correlation ID is a 24-byte array. As a string, the connector represents it using a sequence of 48 hexadecimal characters. The Kafka key will be truncated to fit into this size. The connector can be configured to set the Kafka topic, partition and offset as JMS message properties using the mq.message.builder.*.property configuration values. If configured, the topic is set as a string property, the partition as an integer property and the offset as a long property. Because these values are set using JMS message properties, they only have an effect if mq.message.body.jms=true is set.
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