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.
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".
- 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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