Hi,
Anything involving large amounts of data processing requires data locality. That means any
ETL jobs (large buckets queries, ETL compression jobs..) really benefit from locality
since it doesn't have to move large amounts of data from machine to machine. As for
Hawkular-Metrics being resource heavy, it's not if we decide not (the actual core of
metrics runs just fine on ~50MB of RAM while processing inserts, but the REST-interface
due to Wildfly eats hundreds of megabytes).
Not every (any) node needs a Wildfly.
This is quite similar to how Spark is ran on the Cassandras, the Datastax supported way is
running one Spark per one Cassandra node. And same for their Solr solution. And this is
also one of the core ideas behind HDFS..
- Micke
----- Original Message -----
From: "Matt Wringe" <mwringe(a)redhat.com>
To: "Discussions around Hawkular development"
<hawkular-dev(a)lists.jboss.org>
Sent: Tuesday, September 6, 2016 6:45:09 PM
Subject: Re: [Hawkular-dev] managing cassandra cluster
----- Original Message -----
From: "Michael Burman" <miburman(a)redhat.com>
To: "Discussions around Hawkular development"
<hawkular-dev(a)lists.jboss.org>
Sent: Tuesday, 6 September, 2016 11:09:45 AM
Subject: Re: [Hawkular-dev] managing cassandra cluster
Hi,
Well, actually I would say for Openshift we should try to hit a single
container strategy, which has both HWKMETRICS & Cassandra deployed. This is
to ensure that some of the operations we're going to do in the future can
take advantage of data locality.
Hawkular Metrics and Cassandra are not cheap components to run in a cluster, they take up
a lot of resources. If we can run multiple Cassandras for every 1 Hawkular (or vice versa)
then we can use a lot less resources than if we always need to scale both up because one
has become a bottleneck.
I would really not want to bundle these together if we don't have to.
What operations would require bundling them together exactly?
So unless we run a separate service inside the Cassandra containers,
there's
no easy single metric to get from Cassandra that provides "needs scaling".
This is what I was expecting, I was not expecting Cassandra itself to provide a nice
default metric to scale or not based on a specific value. We would have some service
running along with Cassandra monitoring itself and the cluster to determine if scaling is
required or not.
- Micke
----- Original Message -----
From: "Matt Wringe" <mwringe(a)redhat.com>
To: "Discussions around Hawkular development"
<hawkular-dev(a)lists.jboss.org>
Sent: Tuesday, September 6, 2016 5:26:07 PM
Subject: Re: [Hawkular-dev] managing cassandra cluster
----- Original Message -----
> From: "John Sanda" <jsanda(a)redhat.com>
> To: "Discussions around Hawkular development"
> <hawkular-dev(a)lists.jboss.org>
> Sent: Friday, 2 September, 2016 11:34:07 AM
> Subject: [Hawkular-dev] managing cassandra cluster
>
> To date we haven’t really done anything by way of managing/monitoring the
> Cassandra cluster. We need to monitor Cassandra in order to know things
> like:
>
> * When additional nodes are needed
> * When disk space is low
> * When I/O is too slow
> * When more heap space is needed
>
> Cassandra exposes a lot of metrics. I created HWKMETRICS-448. It briefly
> talks about collecting metrics from Cassandra. In terms of managing the
> cluster, I will provide a few concrete examples that have come up recently
> in OpenShift.
>
> Scenario 1: User deploys additional node(s) to reduce the load on cluster
> After the new node has bootstrapped and is running, we need to run nodetool
> cleanup on each node (or run it via JMX) in order to remove keys/data that
> each each node no longer owns; otherwise, disk space won’t be freed up. The
> cleanup operation can potentially be resource intensive as it triggers
> compactions. Given this, we probably want to run it one node at a time.
> Right now the user is left to do this manually.
>
> Scenario 2: User deploys additional node(s) to get replication and fault
> tolerance
> I connect to Cassandra directly via cqlsh and update replication_factor. I
> then need to run repair on each node can be tricky because 1) it is
> resource
> intensive, 2) can take a long time, 3) prone to failure, and 4) Cassandra
> does not give progress indicators.
>
> Scenario 3: User sets up regularly, scheduled repair to ensure data is
> consistent across cluster
> Once replication_factor > 1, repair needs to be run on a regular basis.
> More
> specifically it should be run within gc_grace_seconds which is configured
> per table and defaults to 10 days. The data table in metrics has reduced
> gc_grace_seconds to 1 day and probably reduce it to zero since it is
> append-only. The value for gc_grace_seconds might vary per table based on
> access patterns, which means the frequency of repair should vary as well.
>
>
> There has already been some discussion of these things for Hawkular Metrics
> in the context of OpenShift. It applies to all of Hawkular Services as
> well.
> Initially I was thinking about building some management components directly
> in metrics, but it probably makes more sense as a separate, shared
> component
> (or components) that can be reused in both stand alone metrics in OpenShift
> and a full Hawkular Services deployment in MiQ for example.
On OpenShift, the ideal situation here would be to have the Cassandra
instances themselves expose a metric that we can use to determine when the
Cassandra cluster is under too much load and needs to scale up. The HPA
would then read this metric and automatically scale the cluster up if
needed.
If we determine that cannot be done for whatever reason and that Hawkular
Metrics needs to determine when to scale or not, there are ways we can do
this. But it gets a little bit more tricky. If given the right permissions
we can go out to the cluster and do things like scale up components, perform
operations of the Cassandra containers directly, etc. Ideally the HPA should
be handling this, but we could get around it if absolutely needed.
>
> We are already running into these scenarios in OpenShift and probably need
> to
> start putting something in place sooner rather than later.
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>
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