The spring boot application that I am working on
pools 1000 messages from table X [ This table X is populated by another service s1]
From each message get the account number and query table Y to get additional information about account.
I am using spring integrating to pool messages from table X and reading additional information for account, I am planning to use Spring JDBC.
We are expecting about 10k messages very day.
Is above approach, to query table Y for each message, a good approach ?
No, that indeed not. If all of that data is in the same database, consider to write a proper SELECT to join those tables in a single query performed by that source polling channel adapter.
Another approach is to implement a stored procedure which will do that job for you and will return the whole needed data: https://docs.spring.io/spring-integration/reference/html/jdbc.html#stored-procedures.
Although if the memory for that number of records to handle at once is a limit in your environment or you don't care how fast all of them are processed, then indeed an integration flow with parallel processing of splitted polling result is OK. For that goal you can use a JdbcOutboundGateway as a service in your flow instead of playing with plain JdbcTemplate: https://docs.spring.io/spring-integration/reference/html/jdbc.html#jdbc-outbound-gateway
Related
I am new to Kafka in Spring Boot, I have been through many tutorials and got fair knowledge about the same.
Currently I have been assigned a task and I am facing an issue. Hope to get some help here.
The scenario is as follows.
1)I have a DB which is getting updated continuously with millions of data.
2)I have to hit the DB after every 5 mins and pick the recently updated data and send it to Kafka.
Condition- The old data that I have picked in my previous iteration should not be picked in my next DB call and Kafka pushing.
I am done with the part of Spring Scheduling to pick the data by using findAll() of spring boot JPA, but how can I write the logic so that it does not pick the old DB records and just take the new record and push it to kafka.
My DB table also have a field called "Recent_timeStamp" of type "datetime"
Its hard to tell without really seeing your logic and the way you work with the database, but from what you've described you should do just "findAll" here.
Instead you should treat your DB table as a time-driven data:
Since it has a field of timestamp, make sure there is an index on it
Instead of "findAll" execute something like:
SELECT <...>
FROM <YOUR_TABLE>
WHERE RECENT_TIMESTAMP > ?
ORDER BY RECENT_TIMESTAMP ASC
In this case you'll get the records ordered by the increasing timestamp
Now the ? denotes the last memorized timestamp that you've handled
So you'll have to maintain the state here
Another option is to query the data whose timestamp is "less" than 5 minutes, in this case the query will look like this (pseudocode since the actual syntax varies):
SELECT <...>
FROM <YOUR_TABLE>
WHERE RECENT_TIMESTAMP < now() - 5 minutes
ORDER BY RECENT_TIMESTAMP ASC
The first method is more robust because if your spring boot application is "down" for some reason you'll be able to recover and query all your records from the point it has failed to send the data. On the other hand you'll have to save this kind of pointer in some type of persistent storage.
The second solution is "easier" in a sense that you don't have a state to maintain but on the other hand you will miss the data after the restart.
In both of the cases you might want to use some kind of pagination because basically you don't know how many records you'll get from the database and if the amount of records exceeds your memory limits, the application with end up with OutOfMemory error thrown.
A Completely different approach is throwing the data to kafka when you write to the database instead of when you read from it. At that point you might have a data chunk of (probably) reasonably limited size and in general you don't need the state because you can store to db and send to kafka from the same service, if the architecture of your application permits to do so.
You can look into kafka connect component if it serves your purpose.
Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka® and other data systems. It makes it simple to quickly define connectors that move large data sets in and out of Kafka. Kafka Connect can ingest entire databases or collect metrics from all your application servers into Kafka topics, making the data available for stream processing with low latency. An export connector can deliver data from Kafka topics into secondary indexes like Elasticsearch, or into batch systems–such as Hadoop for offline analysis.
I have the following scenario. In a database A I have a table with huge amount of records (several millions); these records increase day by day very rapidly (also 100.000 records at day).
I need to fetch these records, check if these records are valid and import them in my own database. At the first interaction I should take all the stored records. Then I can take only the new records saved. I have a timestamp column I can use for this filter but I can't figure how to create a JpaPagingItemReader or a JdbcPagingItemReader and pass the dynamic filter based on the date (e.g. select all records where timestamp is greater than job last execution date)
I'm using spring boot, spring data jpa and spring batch.I'm configuring the Job instance in chunks with dimension 1000. I can also use a paging query (is it useful if I use chunks?)
I have a micro service (let's call this MSA) with all the business logic needed to check if records are valid and insert the valid records.
I have another service on a separate server. This service contains all the batch operation (let's call this MSB).
I'm wondering what is the best approach to the batch. I was thinking to these solutions:
in MSB I duplicate all the entities, repositories and services I use in the MSA. Then in MSB I can make all needed queries
in MSA I create all the rest API needed. The ItemProcessor of MSB will call these rest API to perform checks on items to be processed and finally in the ItemWriter I'll call the rest API for saving data
The first solution would avoid the http calls but it forces me to duplicate all repositories and services between the 2 micro services. Sadly I can't use a common project where to place all the common objects.
The second solution, on the other hand, would avoid the code duplication but it would imply a lot of http calls (above all in the ItemProcessor to check if an item is valid or less).
Do you have any other suggestion? Is there a better approach?
Thank you
Angelo
Could anyone provide an explanation or point me to a good source where it is explained the impact of long lived database transactions when there are other transactions involved?
I'm having difficulties trying to understand what is the real impact in the performance of an application of having transactions where most of the queries are reads and maybe a couple or three are writes, given the different isolation levels.
Mostly I would like to understand it in the situation where:
Neither the rows read nor the rows updated are involved in any other transaction.
The rows read are involved in another transaction but not the rows being updated and this other transaction is read only.
The rows read are involved in another transaction but not the rows being updated and this other transaction is modifying some data being read. I understand here it also affects whether the data is read before or after is being modified.
Both the rows read and the rows updated are involved in another transaction also modifying the data.
These questions come in the context of an application using micro services where all application layer services are annotated with #Transactional using JPA and PostgreSQL and, to transform the data, they need to do some network calls to other micro services within the transaction to fetch some other values.
I have 10 large files in production, and we need to read each line from the file and convert comma separated values into some value object and send it to JMS queue and also insert into 3 different table in the database
if we take 10 files we will have 33 million lines. We are using spring batch(MultiResourceItemReader) to read the earch line and have write to write it o db and also send it to JMS. it roughly takes 25 hrs to completed all.
Eventhough we have 10 system in production, presently we use only one system to run this job( i am new to spring batch, and not aware how spring supports in load balancing)
Since we have only one system we configured data source to connect to db and max connection is specified as 25.
To improve the performance we thought to use spring multi thread support. started to use 5 threads. we could see the performance improvement and could see everything completed in 10 hours.
Here i Have below questions:
1) if i process using 5 threads, we will publish huge amount of data into JMS queue. Will queue support huge data.Note we have 10 systems in production to read JMS Message from the queue.
2) Using thread(5) and 1 production system is good approach (or) instead of spring batch insert the data into db i can create a rest service and spring batch calls the rest api to insert the data into db and let spring api inserts data into JmS queue(again, if spring batch process file annd use rest to insert data into db, per second i will read 4 or 5 lines and will call the rest api. Note we have 10 production system). If use rest API approach will my system support(rest can handle huge request using load balancer, and also JMS can handle huge and huge message) or using thread in spring batch app using 1 production system is better approach.
Different JMS providers are going to have different limits, but in general messaging can easily handle millions of rows in a small period of time.
Messaging is going to be faster than inserting directly into the database because a message has very little data to manage (other than JMS properties) instead of the overhead of a complete RDBMS or NoSQL database or whatever, messaging out performs them all.
Assuming the individual lines can be processed in any order, then sending all data to the same queue and have n consumers working the back-end is a sound solution.
Your big bottleneck, however, is getting the data into the database. If the destination table(s) have m/any keys/indices on them, there is going to be serious contention because each insert/update/delete needs to rebuild the indices, so even though you have n different consumers trying to update the database, they're going to trounce on each other as the transactions are completed.
One solution I've seen is disabling all database constrains before you start and enabling at the end, and hopefully if things worked the data is consistent and usable; of course, the risk is there was bad data that you didn't catch and now you need to clean up or reattempt the load
A better solution might be to transform the files into a single file that can be batch loaded into the database using a platform-specific tool. These tools often disable indexes, contraint checking, and anything else that's going to slow things down - often times bypassing SQL itself - to get performance.
Here is what we came up with. By using 3 value status column.
0 = Not indexed
1 = Updated
2 = Indexed
There will be 2 jobs...
Job 1 will select top X records where status = 0 and pop them into a queue like RabitMQ.
Then a consumer will bulk insert those records to ES and update the status of DB records to 1.
For updates, since we have control of our data... The SQL stored proc that updates that particular record will set it's status to 2. Job2 will select top x records where status = 2 and pop them on RabitMQ. Then a consumer will bulk insert those records to ES and update the status of DB records to 1.
Of course we may need an intermediate status for "queued" so none of the jobs pick up the same record again but the same job should not run if it hasn't completed. The chances of a queued record being updated are slim to none. Since updates only happen at end of day usually the next day.
So I know there's rivers (but being deprecated and probably not flexible like ETL)
I would like to bulk insert records from my SQL server to Elasticsearch.
Write a scheduled batch job of some sort either ETL or any other tool doesn't matter.
select from table where id > lastIdInsertedToElasticSearch this will allow to load the latest records into Elasticsearch at scheduled interval.
But what if a record is updated in the SQL server? What would be a good pattern to track updated records in the SQL server and then push the updated records in ES? I know ES has document versions when putting the same Id. But can't seem to be able to visualize a pattern.
So IMHO, batch inserts are good for building or re-building the index. So for the first time, you can run batch jobs that run SQL queries and perform bulk updates. Rivers, as you correctly pointed out, don't provide a lot of flexibility in terms of transformation.
If the entries in your SQL data store are created by you (i.e. some codebase in your control), it would be better that the same code base updates documents in Elasticsearch, may be not directly but by notifying some other service or with the help of queues to not waste time in responding to requests (if that's the kind of setup you have).
We have a pretty similar use case of Elasticsearch. We provide search inside our app, which performs search across different categories of data. Some of this data is actually created by the users of our app through our app - so we handle this easily. Our app writes that data to our SQL data store and pushes the same data in RabbitMQ for indexing/updating in Elasticsearch. On the other side of RabbitMQ, we have a consumer written in Python that basically replaces the entire document in Elasticsearch. So the corresponding rows in our SQL datastore and documents in Elasticsearch share the ID which enables us to update the document.
Another case is where there are a few types of data that we perform search on comes from some 3rd party service which exposes the data over their HTTP API. The data creation is in our control but we don't have an automated mechanism of updating the entries in Elasticsearch. In this case, we basically run a cron job that takes care of this. We have managed to tune the cron's schedule because we also have a limited number of API queries quota. But in this case, our data is not really updated so much per day. So this kind of system works for us.
Disclaimer: I co-developed this solution.
I needed something like the jdbc-river that could do more complex "roll-ups" of data. After careful consideration of what it would take to modify the jdbc-river to suit my needs, I ended up writing the river-net.
Here are a few of the features:
It gets fairly decent performance (comparable to the jdbc-river. We get upwards of 6k rows/sec)
It can join many tables to create complex nested arrays of documents without creating duplicate child documents
It follows a lot of the same conventions as the jdbc-river.
It also supports reading from files.
It's written in C#
It uses Quartz.Net and supports cron expressions for scheduling.
This project is open source, and we already have a second project (also to be open sourced) that does generic job scheduling with RabbitMQ. We have ported over a lot of this project, and plan to the RabbitMQ river for better performance and stability when indexing into Elasticsearch.
To combat large updates, we aren't hitting tables directly. Instead we use stored procedures that only grab deltas. We also have an option on the sp to reset the delta to reindex everything.
The project is fairly young with only a few commits, but we are open to collaboration and new ideas.