One thing to keep in mind is the overhead of a batch goes up as the number
of servers increases. Talking to 3 is going to have a much different
performance profile than talking to 20. Keep in mind that the coordinator
is going to be talking to every server in the cluster with a big batch.
The amount of local writes will decrease as it owns a smaller portion of
the ring. All you've done is add an extra network hop between your client
and where the data should actually be. You also start to have an impact on
GC in a very negative way.
Your point is valid about topology changes, but that's a relatively rare
occurrence, and the driver is notified pretty quickly, so I wouldn't
optimize for that case.
Can you post your test code in a gist or something? I can't really talk
about your benchmark without seeing it and you're basing your stance on the
premise that it is correct, which it may not be.
On Sat Dec 13 2014 at 8:45:21 AM Eric Stevens <migh...@gmail.com> wrote:
> You can seen what the partition key strategies are for each of the tables,
> test5 shows the least improvement. The set (aid, end) should be unique,
> and bckt is derived from end. Some of these layouts result in clustering
> on the same partition keys, that's actually tunable with the "~15 per
> bucket" reported (exact number of entries per bucket will vary but should
> have a mean of 15 in that run - it's an input parameter to my tests).
> "test5" obviously ends up being exclusively unique partitions for each
> record.
>
> Your points about:
> 1) Failed batches having a higher cost than failed single statements
> 2) In my test, every node was a replica for all data.
>
> These are both very good points.
>
> For #1, since the worst case scenario is nearly twice fast in batches as
> its single statement equivalent, in terms of impact on the client, you'd
> have to be retrying half your batches before you broke even there (but of
> course those retries are not free to the cluster, so you probably make the
> performance tipping point approach a lot faster). This alone may be cause
> to justify avoiding batches, or at least severely limiting their size (hey,
> that's what this discussion is about!).
>
> For #2, that's certainly a good point, for this test cluster, I should at
> least re-run with RF=1 so that proxying times start to matter. If you're
> not using a token aware client or not using a token aware policy for
> whatever reason, this should even out though, no? Each node will end up
> coordinating 1/(nodecount-rf+1) mutations, regardless of whether they are
> batched or single statements. The DS driver is very careful to caution
> that the topology map it maintains makes no guarantees on freshness, so you
> may see a significant performance penalty in your client when the topology
> changes if you're depending on token aware routing as part of your
> performance requirements.
>
>
> I'm curious what your thoughts are on grouping statements by primary
> replica according to the routing policy, and executing unlogged batches
> that way (so that for token aware routing, all statements are executed on a
> replica, for others it'd make no difference). Retries are still more
> expensive, but token aware proxying avoidance is still had. It's pretty
> easy to do in Scala:
>
> def groupByFirstReplica(statements: Iterable[Statement])(implicit
> session: Session): Map[Host, Seq[Statement]] = {
> val meta = session.getCluster.getMetadata
> statements.groupBy { st =>
> meta.getReplicas(st.getKeyspace, st.getRoutingKey).iterator().next
> }
> }
> val result =
> Future.traverse(groupByFirstReplica(statements).values).map(st =>
> newBatch(st).executeAsync())
>
>
> Let me get together my test code, it depends on some existing utilities we
> use elsewhere, such as implicit conversions between Google and Scala native
> futures. I'll try to put this together in a format that's runnable for you
> in a Scala REPL console without having to resolve our internal
> dependencies. This may not be today though.
>
> Also, @Ryan, I don't think that shuffling would make a difference for my
> above tests since as Jon observed, all my nodes were already replicas there.
>
>
> On Sat, Dec 13, 2014 at 7:37 AM, Ryan Svihla <rsvi...@datastax.com> wrote:
>
>> Also..what happens when you turn on shuffle with token aware?
>> http://www.datastax.com/drivers/java/2.1/com/datastax/driver/core/policies/TokenAwarePolicy.html
>>
>> On Sat, Dec 13, 2014 at 8:21 AM, Jonathan Haddad <j...@jonhaddad.com>
>> wrote:
>>>
>>> To add to Ryan's (extremely valid!) point, your test works because the
>>> coordinator is always a replica. Try again using 20 (or 50) nodes.
>>> Batching works great at RF=N=3 because it always gets to write to local and
>>> talk to exactly 2 other servers on every request. Consider what happens
>>> when the coordinator needs to talk to 100 servers. It's unnecessary
>>> overhead on the server side.
>>>
>>> To save network overhead, Cassandra 2.1 added support for response
>>> grouping (see
>>> http://www.datastax.com/dev/blog/cassandra-2-1-now-over-50-faster)
>>> which massively helps performance. It provides the benefit of batches but
>>> without the coordinator overhead.
>>>
>>> Can you post your benchmark code?
>>>
>>> On Sat Dec 13 2014 at 6:10:36 AM Jonathan Haddad <j...@jonhaddad.com>
>>> wrote:
>>>
>>>> There are cases where it can. For instance, if you batch multiple
>>>> mutations to the same partition (and talk to a replica for that partition)
>>>> they can reduce network overhead because they're effectively a single
>>>> mutation in the eye of the cluster. However, if you're not doing that (and
>>>> most people aren't!) you end up putting additional pressure on the
>>>> coordinator because now it has to talk to several other servers. If you
>>>> have 100 servers, and perform a mutation on 100 partitions, you could have
>>>> a coordinator that's
>>>>
>>>> 1) talking to every machine in the cluster and
>>>> b) waiting on a response from a significant portion of them
>>>>
>>>> before it can respond success or fail. Any delay, from GC to a bad
>>>> disk, can affect the performance of the entire batch.
>>>>
>>>>
>>>> On Sat Dec 13 2014 at 4:17:33 AM Jack Krupansky <
>>>> j...@basetechnology.com> wrote:
>>>>
>>>>> Jonathan and Ryan,
>>>>>
>>>>> Jonathan says “It is absolutely not going to help you if you're trying
>>>>> to lump queries together to reduce network & server overhead - in fact
>>>>> it'll do the opposite”, but I would note that the CQL3 spec says “The
>>>>> BATCH statement ... serves several purposes: 1. It saves network
>>>>> round-trips between the client and the server (and sometimes between the
>>>>> server coordinator and the replicas) when batching multiple updates.” Is
>>>>> the spec inaccurate? I mean, it seems in conflict with your statement.
>>>>>
>>>>> See:
>>>>> https://cassandra.apache.org/doc/cql3/CQL.html
>>>>>
>>>>> I see the spec as gospel – if it’s not accurate, let’s propose a
>>>>> change to make it accurate.
>>>>>
>>>>> The DataStax CQL doc is more nuanced: “Batching multiple statements
>>>>> can save network exchanges between the client/server and server
>>>>> coordinator/replicas. However, because of the distributed nature of
>>>>> Cassandra, spread requests across nearby nodes as much as possible to
>>>>> optimize performance. Using batches to optimize performance is usually not
>>>>> successful, as described in Using and misusing batches section. For
>>>>> information about the fastest way to load data, see "Cassandra: Batch
>>>>> loading without the Batch keyword."”
>>>>>
>>>>> Maybe what we really need is a “client/driver-side batch”, which is
>>>>> simply a way to collect “batches” of operations in the client/driver and
>>>>> then let the driver determine what degree of batching and asynchronous
>>>>> operation is appropriate.
>>>>>
>>>>> It might also be nice to have an inquiry for the cluster as to what
>>>>> batch size is most optimal for the cluster, like number of mutations in a
>>>>> batch and number of simultaneous connections, and to have that be dynamic
>>>>> based on overall cluster load.
>>>>>
>>>>> I would also note that the example in the spec has multiple inserts
>>>>> with different partition key values, which flies in the face of the
>>>>> admonition to to refrain from using server-side distribution of requests.
>>>>>
>>>>> At a minimum the CQL spec should make a more clear statement of intent
>>>>> and non-intent for BATCH.
>>>>>
>>>>> -- Jack Krupansky
>>>>>
>>>>> *From:* Jonathan Haddad <j...@jonhaddad.com>
>>>>> *Sent:* Friday, December 12, 2014 12:58 PM
>>>>> *To:* user@cassandra.apache.org ; Ryan Svihla <rsvi...@datastax.com>
>>>>> *Subject:* Re: batch_size_warn_threshold_in_kb
>>>>>
>>>>> The really important thing to really take away from Ryan's original
>>>>> post is that batches are not there for performance. The only case I
>>>>> consider batches to be useful for is when you absolutely need to know that
>>>>> several tables all get a mutation (via logged batches). The use case for
>>>>> this is when you've got multiple tables that are serving as different
>>>>> views
>>>>> for data. It is absolutely not going to help you if you're trying to lump
>>>>> queries together to reduce network & server overhead - in fact it'll do
>>>>> the
>>>>> opposite. If you're trying to do that, instead perform many async
>>>>> queries. The overhead of batches in cassandra is significant and you're
>>>>> going to hit a lot of problems if you use them excessively (timeouts /
>>>>> failures).
>>>>>
>>>>> tl;dr: you probably don't want batch, you most likely want many async
>>>>> calls
>>>>>
>>>>> On Thu Dec 11 2014 at 11:15:00 PM Mohammed Guller <
>>>>> moham...@glassbeam.com> wrote:
>>>>>
>>>>>> Ryan,
>>>>>>
>>>>>> Thanks for the quick response.
>>>>>>
>>>>>>
>>>>>>
>>>>>> I did see that jira before posting my question on this list. However,
>>>>>> I didn’t see any information about why 5kb+ data will cause instability.
>>>>>> 5kb or even 50kb seems too small. For example, if each mutation is 1000+
>>>>>> bytes, then with just 5 mutations, you will hit that threshold.
>>>>>>
>>>>>>
>>>>>>
>>>>>> In addition, Patrick is saying that he does not recommend more than
>>>>>> 100 mutations per batch. So why not warn users just on the # of mutations
>>>>>> in a batch?
>>>>>>
>>>>>>
>>>>>>
>>>>>> Mohammed
>>>>>>
>>>>>>
>>>>>>
>>>>>> *From:* Ryan Svihla [mailto:rsvi...@datastax.com]
>>>>>> *Sent:* Thursday, December 11, 2014 12:56 PM
>>>>>> *To:* user@cassandra.apache.org
>>>>>> *Subject:* Re: batch_size_warn_threshold_in_kb
>>>>>>
>>>>>>
>>>>>>
>>>>>> Nothing magic, just put in there based on experience. You can find
>>>>>> the story behind the original recommendation here
>>>>>>
>>>>>>
>>>>>>
>>>>>> https://issues.apache.org/jira/browse/CASSANDRA-6487
>>>>>>
>>>>>>
>>>>>>
>>>>>> Key reasoning for the desire comes from Patrick McFadden:
>>>>>>
>>>>>>
>>>>>> "Yes that was in bytes. Just in my own experience, I don't recommend
>>>>>> more than ~100 mutations per batch. Doing some quick math I came up with
>>>>>> 5k
>>>>>> as 100 x 50 byte mutations.
>>>>>>
>>>>>> Totally up for debate."
>>>>>>
>>>>>>
>>>>>>
>>>>>> It's totally changeable, however, it's there in no small part because
>>>>>> so many people confuse the BATCH keyword as a performance optimization,
>>>>>> this helps flag those cases of misuse.
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Thu, Dec 11, 2014 at 2:43 PM, Mohammed Guller <
>>>>>> moham...@glassbeam.com> wrote:
>>>>>>
>>>>>> Hi –
>>>>>>
>>>>>> The cassandra.yaml file has property called
>>>>>> *batch_size_warn_threshold_in_kb.
>>>>>> *
>>>>>>
>>>>>> The default size is 5kb and according to the comments in the yaml
>>>>>> file, it is used to log WARN on any batch size exceeding this value in
>>>>>> kilobytes. It says caution should be taken on increasing the size of this
>>>>>> threshold as it can lead to node instability.
>>>>>>
>>>>>>
>>>>>>
>>>>>> Does anybody know the significance of this magic number 5kb? Why
>>>>>> would a higher number (say 10kb) lead to node instability?
>>>>>>
>>>>>>
>>>>>>
>>>>>> Mohammed
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>>
>>>>>> [image: datastax_logo.png] <http://www.datastax.com/>
>>>>>>
>>>>>> Ryan Svihla
>>>>>>
>>>>>> Solution Architect
>>>>>>
>>>>>>
>>>>>> [image: twitter.png] <https://twitter.com/foundev>[image:
>>>>>> linkedin.png] <http://www.linkedin.com/pub/ryan-svihla/12/621/727/>
>>>>>>
>>>>>>
>>>>>>
>>>>>> DataStax is the fastest, most scalable distributed database
>>>>>> technology, delivering Apache Cassandra to the world’s most innovative
>>>>>> enterprises. Datastax is built to be agile, always-on, and predictably
>>>>>> scalable to any size. With more than 500 customers in 45 countries,
>>>>>> DataStax
>>>>>> is the database technology and transactional backbone of choice for the
>>>>>> worlds most innovative companies such as Netflix, Adobe, Intuit, and
>>>>>> eBay.
>>>>>>
>>>>>>
>>>>>>
>>>>>
>>
>> --
>>
>> [image: datastax_logo.png] <http://www.datastax.com/>
>>
>> Ryan Svihla
>>
>> Solution Architect
>>
>> [image: twitter.png] <https://twitter.com/foundev> [image: linkedin.png]
>> <http://www.linkedin.com/pub/ryan-svihla/12/621/727/>
>>
>> DataStax is the fastest, most scalable distributed database technology,
>> delivering Apache Cassandra to the world’s most innovative enterprises.
>> Datastax is built to be agile, always-on, and predictably scalable to any
>> size. With more than 500 customers in 45 countries, DataStax is the
>> database technology and transactional backbone of choice for the worlds
>> most innovative companies such as Netflix, Adobe, Intuit, and eBay.
>>
>>
>
