Andrei, Evgenii, thanks for answer.

Aa far as I see, there is no ready to use tutorial. I managed to do multi-threaded cache load procedure, out-of-the-box loadCache method is extremely slow.

I spent about a month studying write-through topics, and finally got the same as "capacity planning" says: 0.8Gb mssql table on disk expands to 2.3Gb, size in ram is 2.875 times bigger.

Is it beneficial to use BinaryObject instead of user pojo? If yes, how to create BinaryObject without pojo definition and deserialize it back to pojo?
It would be great to have kind of advanced github example like this 

https://github.com/dmagda/MicroServicesExample

It helped a lot in understanding. Current documentation links do not help to build a real solution, they are mostly like a reference, with no option to compile and debug

Vladimir

2:51, 11 марта 2020 г., Evgenii Zhuravlev <[email protected]>:
When you're saying that the result was poor, do you mean that data preloading took too much time, or it's just about get operations?

Evgenii

вт, 10 мар. 2020 г. в 03:29, aealexsandrov <[email protected]>:
Hi,

You can read the documentation articles:

https://apacheignite.readme.io/docs/3rd-party-store

In case if you are going to load the cache from 3-rd party store (RDBMS)
then the default implementation of CacheJdbcPojoStore can take a lot of time
for loading the data because it used JDBC connection inside (not pull of
these connections).

Probably you should implement your own version of CacheStore that will read
data from RDBMS in several threads, e.g using the JDBC connection pull
there. Sources are open for you, so you can copy the existed implementation
and modify it:

https://github.com/apache/ignite/blob/master/modules/core/src/main/java/org/apache/ignite/cache/store/jdbc/CacheJdbcPojoStore.java

Otherwise, you can do the initial data loading using some streaming tools:

1)Spark integration with Ignite -
https://apacheignite-fs.readme.io/docs/ignite-data-frame
2)Kafka integration with Ignite -
https://apacheignite-mix.readme.io/docs/kafka-streamer

BR,
Andrei



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