mispoke "That's all correct but what you're not accounting for is if you use a token aware client then the coordinator will likely not own all the data in a batch"
should just be "That's all correct but what you're not accounting for is the coordinator will likely not own all the data in a batch" Token awareness has no effect on that fact. On Tue, Dec 2, 2014 at 9:13 AM, Ryan Svihla <rsvi...@datastax.com> wrote: > > > On Mon, Dec 1, 2014 at 1:52 PM, Dong Dai <daidon...@gmail.com> wrote: > >> Thanks Ryan, and also thanks for your great blog post. >> >> However, this makes me more confused. Mainly about the coordinators. >> >> Based on my understanding, no matter it is batch insertion, ordinary sync >> insert, or async insert, >> the coordinator was only selected once for the whole session by calling >> cluster.connect(), and after >> that, all the insertions will go through that coordinator. >> > > That's all correct but what you're not accounting for is if you use a > token aware client then the coordinator will likely not own all the data in > a batch, ESPECIALLY as you scale up to more nodes. If you are using > executeAsync and a single row then the coordinator node will always be an > owner of the data, thereby minimizing network hops. Some people now stop me > and say "but the client is making those hops!", and that's when I point out > "what do you think the coordinator has to do", only you've introduced > something in the middle, and prevent token awareness from doing it's job. > The savings in latency are particularly huge if you use more than a > consistency level one on your write. > > >> If this is not the case, and the clients do more work, like distribute >> each insert to different >> coordinators based on its partition key. It is understandable the large >> volume of UNLOGGED BATCH >> will cause some bottleneck in the coordinator server. However, this >> should be not hard to solve by distributing >> insertions in one batch into different coordinators based on partition >> keys. I will be curious why >> this is not supported. >> > > The coordinator node does this of course today, but this is the very > bottleneck of which you refer. To do what you're wanting to do and make it > work, you'd have to enhance the CLIENT to make sure that all the objects in > that batch were actually owned by the coordinator itself, and if you're > talking about parsing a CQL BATCH on the client and splitting it out to the > appropriate nodes in some sort of hyper token awareness, then you're taking > a server side responsibility (CQL parsing) and moving it to the client. > Worse you're asking for a number of bugs to occur by moving CQL parsing to > the client, IE do all clients handle this the same way? what happens to > older thrift clients with batch?, etc, etc, etc. > > Final point, every time you do a batch you're adding extra load on the > heap to the coordinator node that could be instead on the client. This > cannot be stated strongly enough. In production doing large batches (say > over 5k) is a wonderful way to make your node spend a lot of it's time > handling batches and the overhead of that process. > >> >> P.S. I have the asynchronous insertion tested, probably because my >> dataset is small. Batch insertion >> is always much better than async insertions. Do you have a general idea >> how large the dataset should be >> to reverse this performance comparison. >> > > You could be in a situation where the node owns all the data, and so can > respond quickly, so it's hard to say, you can see however as the cluster > scales there is no way that a given node will own everything in the batch > unless you've designed it to be that way, either by some token aware batch > generation in the client or by only batching on the same partition key > (strategy covered in that blog). > > PS Every time I've had a customer tell me batch is faster than async, it's > been a code problem such as not storing futures for later, or in Python not > using libev, in all cases I've gotten at least 2x speed up and often way > more. > > >> - Dong >> >> > On Dec 1, 2014, at 9:57 AM, Ryan Svihla <rsvi...@datastax.com> wrote: >> > >> > So there is a bit of a misunderstanding about the role of the >> coordinator >> > in all this. If you use an UNLOGGED BATCH and all of those writes are in >> > the same partition key, then yes it's a savings and acts as one >> mutation. >> > If they're not however, you're asking the coordinator node to do work >> the >> > client could do, and you're potentially adding an extra round hop on >> > several of those transactions if that coordinator node does not happen >> to >> > own that partition key (and assuming your client driver is using token >> > awareness, as it is in recent versions of the DataStax Java Driver. This >> > also says nothing of heap pressure, and the measurable effect of large >> > batches on node performance is in practice a problem in production >> clusters. >> > >> > I frequently have had to switch people off using BATCH for bulk loading >> > style processes and in _every_ single case it's been faster to use >> > executeAsync..not to mention the cluster was healthier as a result. >> > >> > As for the sstable loader options since they all use the streaming >> protocol >> > and as of today the streaming protocol will stream one copy to each >> remote >> > nodes, that they tend to be slower than even executeAsync in multi data >> > center scenarios (though in single data center they're faster options, >> that >> > said..the executeAsync approach is often fast enough). >> > >> > This is all covered in a blog post >> > >> https://medium.com/@foundev/cassandra-batch-loading-without-the-batch-keyword-40f00e35e23e >> > and the DataStax CQL docs also reference BATCH is not a performance >> > optimization >> > >> http://www.datastax.com/documentation/cql/3.1/cql/cql_using/useBatch.html >> > >> > In summary the only way UNLOGGED BATCH is a performance improvement over >> > using async with the driver is if they're within a certain reasonable >> size >> > and they're all to the same partition. >> > >> > On Mon, Dec 1, 2014 at 9:43 AM, Dong Dai <daidon...@gmail.com> wrote: >> > >> >> Thank a lot for the reply, Raj, >> >> >> >> I understand they are different. But if we define a Batch with >> UNLOGGED, >> >> it will not guarantee the atomic transaction, and become more like a >> data >> >> import tool. According to my knowledge, BATCH statement packs several >> >> mutations into one RPC to save time. Similarly, Bulk Loader also pack >> all >> >> the mutations as a SSTable file and (I think) may be able to save lot >> of >> >> time too. >> >> >> >> I am interested that, in the coordinator server, are Batch Insert and >> Bulk >> >> Loader the similar thing? I mean are they implemented in the similar >> way? >> >> >> >> P.S. I try to randomly insert 1000 rows into a simple table on my >> laptop >> >> as a test. Sync Insert will take almost 2s to finish, but sync batch >> insert >> >> only take like 900ms. It is a huge performance improvement, I wonder is >> >> this expected? >> >> >> >> Also, I used CQLSStableWriter to put these 1000 insertions into a >> single >> >> SSTable file, it costs around 2s to finish on my laptop. Seems to be >> pretty >> >> slow. >> >> >> >> thanks! >> >> - Dong >> >> >> >>> On Dec 1, 2014, at 2:33 AM, Rajanarayanan Thottuvaikkatumana < >> >> rnambood...@gmail.com> wrote: >> >>> >> >>> BATCH statement and Bulk Load are totally different things. The BATCH >> >> statement comes in the atomic transaction space which provides a way to >> >> make more than one statements into an atomic unit and bulk loader >> provides >> >> the ability to bulk load external data into a cluster. Two are totally >> >> different things and cannot be compared. >> >>> >> >>> Thanks >> >>> -Raj >> >>> >> >>> On 01-Dec-2014, at 4:32 am, Dong Dai <daidon...@gmail.com> wrote: >> >>> >> >>>> Hi, all, >> >>>> >> >>>> I have a performance question about the batch insert and bulk load. >> >>>> >> >>>> According to the documents, to import large volume of data into >> >> Cassandra, Batch Insert and Bulk Load can both be an option. Using >> batch >> >> insert is pretty straightforwards, but there have not been an >> ‘official’ >> >> way to use Bulk Load to import the data (in this case, i mean the data >> was >> >> generated online). >> >>>> >> >>>> So, i am thinking first clients use CQLSSTableWriter to create the >> >> SSTable files, then use “org.apache.cassandra.tools.BulkLoader” to >> import >> >> these SSTables into Cassandra directly. >> >>>> >> >>>> The question is can I expect a better performance using the >> BulkLoader >> >> this way comparing with using Batch insert? >> >>>> >> >>>> I am not so familiar with the implementation of Bulk Load. But i do >> see >> >> a huge performance improvement using Batch Insert. Really want to know >> the >> >> upper limits of the write performance. Any comment will be helpful, >> Thanks! >> >>>> >> >>>> - Dong >> >>>> >> >>> >> >> >> >> >> > >> > >> > -- >> > >> > [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. > > -- [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.
