I want to do some analytics using Flink on the Data in Postgresql. How and where should I give the port address,username and password. I was trying with the table source as mentioned in the link:https://ci.apache.org/projects/flink/flink-docs-release-1.4/dev/table/common.html#register-tables-in-the-catalog.
final static ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
final static TableSource csvSource = new CsvTableSource("localhost", port);
I am unable to start with actually. I went through all the documents but detailed report about this not found.
The tables and catalog referred to the link you've shared are part of Flink's SQL support, wherein you can use SQL to express computations (queries) to be performed on data ingested into Flink. This is not about connecting Flink to a database, but rather it's about having Flink behave somewhat like a database.
To the best of my knowledge, there is no Postgres source connector for Flink. There is a JDBC table sink, but it only supports append mode (via INSERTs).
The CSVTableSource is for reading data from CSV files, which can then be processed by Flink.
If you want to operate on your data in batches, one approach you could take would be to export the data from Postgres to CSV, and then use a CSVTableSource to load it into Flink. On the other hand, if you wish to establish a streaming connection, you could connect Postgres to Kafka and then use one of Flink's Kafka connectors.
Reading a Postgres instance directly isn't supported as far as I know. However, you can get realtime streaming of Postgres changes by using a Kafka server and a Debezium instance that replicates from Postgres to Kafka.
Debezium connects using the native Postgres replication mechanism on the DB side and emits all record inserts, updates or deletes as a message on the Kafka side. You can then use the Kafka topic(s) as your input in Flink.
Related
How we can stream schema and data changes along with some kind of transformations into another MySQL instance using Kafka connect source connector.
Is there a way to propagate schema changes also if I use Kafka's Python library(confluent_kafka) to consume and transform messages before loading into target DB.
You can use Debezium to stream MySQL binlogs into Kafka. Debezium is built upon Kafka Connect framework.
From there, you can use whatever client you want, including Python, to consume and transform the data.
If you want to write to MySQL, you can use Kafka Connect JDBC sink connector.
Here is an old post on this topic - https://debezium.io/blog/2017/09/25/streaming-to-another-database/
I am looking to write a custom connector for Apache Kafka to connect to SQL database to get CDC data. I would like to write a custom connector so I can connect to multiple databases using one connector because all the marketplace connectors only offer one database per connector.
First question: Is it possible to connect to multiple databases using one custom connector? Also, in that custom connector, can I define which topics the data should go to?
Second question: Can I write a custom connector in .NET or it has to be Java? Is there an example that I can look at for custom connector for CDC for a database in .net?
There are no .NET examples. The Kafka Connect API is Java only, and not specific to Confluent.
Source is here - https://github.com/apache/kafka/tree/trunk/connect
Dependency here - https://search.maven.org/artifact/org.apache.kafka/connect-api
looking to write a custom connector ... to connect to SQL database to get CDC data
You could extend or contribute to Debezium, if you really wanted this feature.
connect to multiple databases using one custom connector
If you mean database servers, then not really, no. Your URL would have to be unique per connector task, and there isn't an API to map a task number to a config value. If you mean one server, and multiple database schemas, then I also don't think that is really possible to properly "distribute" within a single connector with multiple tasks (thus why database.names config in Debezium only currently supports one name).
explored debezium but it won't work for us because we have microservices architecture and we have more than 1000 databases for many clients and debezium creates one topic for each table which means it is going to be a massive architecture
Kafka can handle thousands of topics fine. If you run the connector processes in Kubernetes, as an example, then they're centrally deployable, scalable, and configurable from there.
However, I still have concerns over you needing all databases to capture CDC events.
Was also previously suggested to use Maxwell
I'm currently working in a Mainframe Technology where we store the data in IBM DB2.
We got a new requirement to use scalable process to migrate the data to a new messaging platform including new database. For that we have identified Kafka is a suitable solution with either KSQLDB or MONGODB.
Can someone able to tell or direct me on how can we connect to IBM DB2 from Kafka to import the data and place it in either KSQLDB or MONGODB?
Any help is much appreciated.
To import the data from IBM DB2 into Kafka, You need to use any connector like the Debezium connector for DB2.
The information regarding the connector can be found in the following.
https://debezium.io/documentation/reference/connectors/db2.html
Connector Configuration
You can also use JDBC Source Connector for the same functionality. The following links are helpful for the configuration.
https://www.confluent.io/blog/kafka-connect-deep-dive-jdbc-source-connector/
A Simple diagram for events flows from RDMS to Kafka topic.
After placing the data into Kafka, we need to transfer that data MongoDb. We need to use Mongo Db Connector to transfer the data from Kafka to mongo Db.
https://www.mongodb.com/blog/post/getting-started-with-the-mongodb-connector-for-apache-kafka-and-mongodb-atlas
https://www.confluent.io/hub/mongodb/kafka-connect-mongodb
I have read that an Kafka connect source can receive a multiple topics from a database (a topic represent one table). I have PostgreSQL database with many table, and one Kafka source are satisfied enough for this time. But is it available if I declare only single JDBC Kafka sink to consume all the topics into topic-based destination table, for example all tables from PostgreSQL into single MS SQL Server Database? It is time-cost, for example if I have 200 Tables from one database and must make 200 sinks connection for each tables, although I only need to declare the source once.
You can use Debezium to snapshot one database and all tables to send them over Kafka and dump to any other sink connector (including MSSQL), yes
How many connectors you need to run or how many tables on the destination you'll create are ultimately up to your own configurations
And standalone doesn't matter, but distributed mode is preferred anyway, even if you are only using one machine
I am trying to stream changes in my Postgres database using the Kafka Connect JDBC Connector. I am running into issues upon startup as the database is quite big and the query dies every time as rows change in between.
What is the best practice for starting off the JDBC Connector on really huge tables?
Assuming you can't pause the workload on the database that you're streaming the contents in from to allow the initialisation to complete, I would look at Debezium.
In fact, depending on your use case, I would look at Debezium regardless :) It lets you do true CDC against Postgres (and MySQL and MongoDB), and is a Kafka Connect plugin just like the JDBC Connector is so you retain all the benefits of that.