I have a database say test and we are having multiple Kafka Debezium Connectors on it.
Each connector is associate with one table.
My question is in terms of memory usage, which is a better approach:
One connector per database OR
One connector per table
I think it really depends on your use case. I don't think there is a general approach for all the usecases.
For example, at my current job, we decided to have 4 connectors that stream changes from the same database, but, each of them is streaming from a subset of tables. The main reason is that we don't want to have a single point of failure where a bad record inside DB can break all our usecases that rely on CDC, hence, we divided the tables and assigned them to a connector. Note that it's not good to have a lot of replication slots on the database also. So it really depends on your usecase.
Considering all performance factors, it is always a recommended approach to have a single source connector (multiple instances to share the load), with replicator or configuration file per database instance (test1, test2, test3 etc), having multiple tables, so the data ingress would be 1 table -> 1 topic.
You can have a better view at Oracle Golden Gate implementation pattern for the same.
https://rmoff.net/2018/12/12/streaming-data-from-oracle-into-kafka/
Related
As the title says, I have 2 seperate servers and I want both connectors to read from same source to write to their respective topic. A single connector works well. When I create another one in a different server they seem to be running but no data flow occurs for both.
My question is, is that possible to run 2 debezium connectors that read from same source? I couldn't find any information about this topic in documentation.
Edit: I've tested it with oracle database and never seen it's working well. Definitely wouldn't recommend using it especially in oracle.
So generally speaking, Debezium does not recommend that you use multiple connectors per database source and prefer that you adjust your connector configuration instead. We understand that isn't always the case when you have different business use cases at play.
That said, it's important that if you do deploy multiple connectors you properly configure each connector so that it doesn't share state such as the same database history topic, etc.
For certain database platforms, having multiple source connectors really doesn't apply any real burden to the database, such as MySQL. But other databases like Oracle, running multiple connectors can have a pretty substantial impact.
When an Oracle connector streams changes, it starts an Oracle LogMIner mining session. This session is responsible for loading, reading, parsing, and preparing the contents of the data read in a special in-memory table that the connector uses to generate change events. When you run multiple connectors, you will have concurrent Oracle LogMiner sessions happening and each session will be consuming its own share of PGA memory to support the steps taken by Oracle LogMiner. Depending on your database's volatility, this can be stressful on the database server since Oracle specifically assigns one LogMiner session to a CPU.
For an Oracle environment, I highly recommend you avoid using multiple connectors unless you are needing to stream changes from different PDBs within the same instance since there is really no technical reason why you should want to read, load, parse, and generate change data for the same redo entries multiple times, once per connector deployment.
I have an application which requires an interesting orchestration between states of instances distributed across geographic regions, in combination with the need for a scalable distributed database.
At the moment I think that Kafka with log compaction will fit my needs for state maintenance and message exchange between instances, and Cassandra will fit my needs for high volume distributed reads and writes of persisted data.
However, there is quite a lot of data duplicated that way: Many of the data exchanged via Kafka would also need to be stored to Cassandra for distributed data access. Using Kafka for both messaging and distributed data querying and persistence seems tempting.
Therefore, I'm interested to figure out the real-world pros and cons to be expected when using e.g. the pull queries feature of Kafka to use it as a distributed database [1].
Though, I'm a bit suspicious about what to expect of that in terms of performance and scalability, especially when compared to Cassandra, as well as unknown pitfalls.
What are the tradeoffs when using Kafka as a distributed DB, and what would it compare performance-wise to "native" distributed systems like Cassandra?
[1] https://www.confluent.io/de-de/blog/pull-queries-in-preview-confluent-cloud-ksqdb/
pure KV lookups
Then Kafka StateStores / Interactive Queries can work, but with the caveat that if you use containers and an orchestrator, you need to maintain the state of those stores somewhere on persistent volumes. Otherwise, when the containers move to a fresh host, the streams changelog topic needs to be read from the very beginning, giving you a "cold-start" problem, and you will be unable to query.
Using any database (with persistent storage) will not have this problem, and will always be able to query immediately.
I'm not sure I would suggest Cassandra for strictly KV data, though.
I'm trying to set up Debezium for data change monitoring in this huge database.
Documentation says "Debezium can monitor any number of databases. The number of connectors that can be deployed to a single cluster of Kafka Connect services depends upon upon the volume and rate of events. However, Debezium supports multiple Kafka Connect service clusters and, if needed, multiple Kafka clusters as well."
However, there's no mention about how many connectors is a good practice.
Reading mediums and some use cases, it seems like one connector for a whole database is a suitable option. But if we have a lot of tables and a lot of changing events in a fraction of time, it should become a bottleneck. Or not? I've seen people working with one connector per table too. It would mean a LOT of connectors in this case. If you have an use case concerning heavy databases along with Debezium, could you tell your experiences about connectors ?
(The source database, in this case, are mostly postgres)
Sorry if it's a dumb question. Thank you in advance.
What does cluster mean?
The doc says The Kafka cluster *stores* streams of records in categories called topics.
If it stores, then is it a database?
cluster means multiple machines that "share the load" among them. this is deliberately vague as there are many ways of achieving this.
what is the question here?
(disclaimer - my opinion and generally subjective) its a database in the broadest sense of it stores data, and you can get the data back out. however, since it lacks any real "fetch by PK" or query facilities it makes for a very bad choice of primary storage for most use cases and is usually used as an intermediate bus rather than a source of truth.
I have a java based web application which is using 2 backend database servers of Microsoft SQL (1 server is live database as it is transactional and the other one is reporting database). Lag between transactional and reporting databases is of around 30 minutes and incremental data is loaded using a SQL job which runs every 30 minutes and takes around 20-25 minutes in execution. This job is executing an SSIS package and using this package, data from reporting database is further processed and is stored in HDFS and HBase which is eventually used for analytics.
Now, I want to reduce this lag and to do this, I am thinking of implementing a messaging framework. After doing some research, I learned that Kafka could solve my purpose since Kafka can also work as an ETL tool apart from being a messaging framework.
How should I proceed? should I create topics similar to the table structures in SQL server and perform operations on that? Should I redirect my application to write any change happening in Kafka first and then in Transactional database? Please advise on usage of Kafka considering the mentioned use case.
There's a couple ways to do this that require minimal code, and then there's always the option to write your own code.
(Some coworkers just got finished looking at this, with SQL Server and Oracle, so I know a little about this here)
If you're using the enterprise version of SQL Server you could use Change Data Capture and Confluent Kakfa Connect to read all the changes to the data. This (seems to) require both a Enterprise license and may include some other additional cost (I was fuzzy on the details here. This may have been because we're using an older version of SQL Server or because we have many database servers ).
If you're not / can't use the CDC stuff, Kafka Connect's JDBC support also has a mode where it polls the database for changes. This works best if your records have some kind of timestamp column, but usually this is the case.
A poll only mode without CDC means you won't get every change - ie if you poll every 30 seconds and the record changes twice, you won't get individual messages about this change, but you'll get one message with those two changes, if that makes sense. This is Probably acceptable for your business domain, but something to be aware of.
Anyway, Kafka Connect is pretty cool - it will auto create Kafka topics for you based on your table names, including posting the Avro schemas to Schema Registry. (The topic names are knowable, so if you're in an environment with auto topic creation = false, well you can create the topics manually yourself based on the table names). Starting from no Kafka Connect knowledge it took me maybe 2 hours to figure out enough of the configuration to dump a large SQL Server database to Kafka.
I found additional documentation in a Github repository of a Confluent employee describing all this, with documentation of the settings, etc.
There's always the option of having your web app be a Kafka producer itself, and ignore the lower level database stuff. This may be a better solution, like if a request creates a number of records across the data store, but really it's one related event (an Order may spawn off some LineItem records in your relational database, but the downstream database only cares that an order was made).
On the consumer end (ie "next to" your other database) you could either use Kafka Connect on the other end to pick up changes, maybe even writing a custom plugin if required, or write your own Kafka consumer microservice to put the changes into the other database.