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.
