Hi Jon,
#1 Probably yes, K/V and SQL are among the most used features.
Yes, Ignite can be used instead of Redis as a distributed cache in
some use cases. The API is different though.
#2 Those features are production-ready. Ignite is not based on Spark.
#3 Compute API has map/reduce functionality:
https://ignite.apache.org/docs/latest/distributed-computing/map-reduce
Grouping and filtering can be achieved based on that.
Alternatively, use the SQL engine which performs map/reduce under the
hood.
#4 I'd say the choice between SQL and K/V is about two things - convenience
and performance.
K/V API maps the data to your classes, and it is generally faster than
SQL for individual key operations (get, put, replace).
On the other hand, SQL with proper indexes is faster for complex
queries.
#5 Please check
https://ignite.apache.org/docs/latest/extensions-and-integrations/ignite-for-spark/ignite-dataframe
Pavel
On Wed, Dec 1, 2021 at 1:48 PM Jon Hua <jonn...@gmail.com> wrote:
> Hi community
>
> Today I spent a whole day reading the docs:
> https://ignite.apache.org/docs/latest/
>
> This is a well-written documentation for Ignite, thanks for the work.
> I have several questions that:
>
> #1, Is the most used feature of Ignite the distributed K/V storage? Can I
> treat it as the distributed Redis?
> #2, It says it supports streaming, distributed computing, ML Lib. Are they
> affected by Apache Spark? Are these three features production ready?
> #3, I saw that distributed computing has very few API methods. Will you
> expand them later? For example, map(), reduce(), group(), filter() etc.
> #4, The document says SQL and K/V are essentially the same stuff. So when
> to use SQL and when to use K/V interface?
> #5. Will you support dataframe in future? Yes, both Spark and R have the
> dataframe. The structure is quite easy to load outside data such as CSV,
> JSON etc.
>
> Thank you in advance for any help.
>
> Regards
> Jon Hua
>
