You can find some information about capacity planning here:
https://apacheignite.readme.io/docs/capacity-planning
About your India example you can use affinity keys to keep data together
in groups to avoid network traffic.
https://apacheignite.readme.io/docs/affinity-collocation
Mikael
Den 2019-01-02 kl. 14:44, skrev Clay Teahouse:
Thanks Naveen.
-- Cache Groups: When would I start considering cache groups, if my
system is growing, and sooner or later I will have to add to my caches
and I need to know 1) should I starting grouping now (I'd think yes),
2) if no, when, what number of caches?
-- Capacity Planning: So, there is no guidelines on how to size the
nodes and the physical storage nodes reside on? How do I make sure all
the related data fit the same VM? It can't be the case that I have to
come up with 100s of super size VMs just because I have one instance
with a huge set of entries. For example, if I have millions of entries
for India and only a few for other countries, how do I make sure all
the India related data fits the same VM (to avoid the network) and
have the data for all the small countries fit on the same VM?
-- Pinning the data to cache: the data pinned to on-heap cache does
not get evicted from the memory? I want to see if there is something
similar to Oracle's memory pinning.
-- Read through: How do I know if something on cache or disk (using
native persistence)?
5) Service chaining: Is there an example of service chaining that you
can point me to?
6) How do I implement service pipelining in apache ignite? Would
continuous query be the mechanism? Any examples?
7) Streaming: Are there examples on how to define watermarks, i.e.,
input completeness with regard to the event timestamp?
thank you
Clay
On Tue, Jan 1, 2019 at 11:29 PM Naveen <naveen.band...@gmail.com
<mailto:naveen.band...@gmail.com>> wrote:
Hello
Couple of things I would like to with my experience
1. Cache Groups : Around 100 caches, I do not think we need to go
for Cache
groups, as you mentioned cache groups will have impact on you
read/writes.
However, changing the partition count to 128 from default 1024
would improve
your cluster restart.
2. I doubt if Ignite has any settings we have for this.
3. The only I can think of is to keep the data in on-heap if the
data size
is not so huge.
4. Read through, with native persistence enabled, doing a read to
the disk
will load the cache. But the read is much slower compared with
read from
RAM, by default it does not pre-load the data. If you want to
avoid this you
can pre-load the data programatically and load Memory, good for
even SQL
SELECT as well. But with the 3rd party persistence, we need to
pre-load the
data to make your read work for SQL SELECT.
Thanks
Naveen
--
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