Hi,Please take a look at my post [1]. It is not exactly about you question, but it works in production for row count you mentionRegards,Vladimir[1] https://www.gridgain.com/resources/blog/how-fast-load-large-datasets-apache-ignite-using-key-value-api22:39, 22 января 2021 г., gvaidya :Hi Alex,The
Hi Alex,
The data distribution of itemid is very even. For the ~537K records data set
there are exactly 12 records per itemid for all values of itemid.
I have reviewed all the documentation in the links you provided. I also
switched to jdk8 (i was using jdk15 earlier), set my jvm options per the
Hi,
Can you actually see what the data distribution of itemid is.
AffinityKey will direct Ignite to use that specific column (itemid) for
partition mapping.
If the data in itemid is in a narrow range, then all data will be put into
fewer partitions.
This might affect loading times.
Hi Alex,
I am simply using GridGain/Ignite web console to generate a project for
testing cacheload of a single table from my persistent storage. For
evaluation/testing purposes, I am running a single node in my cluster on a
2CPU 32Gb Windows machine.
For testing, I created 2 projects. Test1 is
Hi,
Can you give more context regarding your data load.
How big is your cluster?* Are the load times comparable if you use only
one node for
both use cases? *
If you remove the affinity column in the second use case, does the load
speed up in a cluster env?
If you lower the amount
I have just began experimenting with Apache Ignite as an IMDG. I have
imported a single table (~42 million, 15 columns - mostly integer/decimal
datatypes) in a new cluster created in GridGain Web console and created a
new JAVA project.
I initially defined the cache as partitioned and did not
