I don't know of any benchmarks vs. HBase bulk loader. Would be interesting, if you could come up with an apples-to-apples test.
100TB binary file cannot be partitioned at all? You're always bound to a single process. Bummer. I guess plan B could be pre-processing the binary file into something splittable. You'll cover the data twice, but if Phoenix encoding really is the current bottleneck, as your mail indicates, then separating the decoding of the binary file from encoding of the Phoenix output should allow for parallelizing the second step and improve the state of things. Mean time, would be good to look at perf improvements of the Phoenix encoding step. Any volunteers lurking about? -n On Thu, Mar 5, 2015 at 1:08 PM, Tulasi Paradarami < [email protected]> wrote: > Gabriel, Nick, thanks for your inputs. My comments below. > > Although it may look as though data is being written over the wire to > > Phoenix, the execution of an upsert executor and retrieval of the > > uncommitted KeyValues is all local (in memory). The code is implemented > in > > this way because JDBC is the general API used within Phoenix -- there > isn't > > direct "convert fields to Phoenix encoding" API, although this is doing > the > > equivalent operation. > > I understand, data processing is in memory but performance can be improved > if there is a direct conversion to Phoenix encoding. > Are there any performance comparison results between phoenix & hbase > bulk-loader? > > Could you give some more information on your performance numbers? For > > example, is this the throughput that you're getting in a single process, > or > > over a number of processes? If so, how many processes? > > Its currently running as a single mapper processing a binary file > (un-splittable). Disk throughput doesn't look to be an issue here. > Production has machines of the same processing capability but obviously > more number of nodes and input files. > > > Also, how many columns are in the records that you're loading? > > The row-size is small: 3 integers for PK, 2 short qualifiers, 1 varchar > qualifier > > What is the current (projected) time required to load the data? > > About 20-25 days > > > What is the minimum allowable ingest speed to be considered satisfactory? > > We would like to finish the load in less than 10-12 days. > > > You can make things go faster by increasing the number of mappers. > > The input file (binary) is not-splittable, a mapper is tied to the specific > file. > > What changes did you make to the map() method? Increased logging, > > performance enhancements, plugging in custom logic, something else? > > I added custom logic to the map() method. > > > > On Thu, Mar 5, 2015 at 7:53 AM, Nick Dimiduk <[email protected]> wrote: > > > Also: how large is your cluster? You can make things go faster by > > increasing the number of mappers. What changes did you make to the map() > > method? Increased logging, performance enhancements, plugging in custom > > logic, something else? > > > > On Thursday, March 5, 2015, Gabriel Reid <[email protected]> wrote: > > > > > Hi Tulasi, > > > > > > Answers (and questions) inlined below: > > > > > > On Thu, Mar 5, 2015 at 2:41 AM Tulasi Paradarami < > > > [email protected] <javascript:;>> > > > wrote: > > > > > > > Hi, > > > > > > > > Here are the details of our environment: > > > > Phoenix 4.3 > > > > HBase 0.98.6 > > > > > > > > I'm loading data to a Phoenix table using the csv bulk-loader (after > > > making > > > > some changes to the map(...) method) and it is processing about > 16,000 > > - > > > > 20,000 rows/sec. I noticed that the bulk-loader spends upto 40% of > the > > > > execution time in the following steps. > > > > > > > > > > //... > > > > csvRecord = csvLineParser.parse(value.toString()); > > > > csvUpsertExecutor.execute(ImmutableList.of(csvRecord)); > > > > Iterator<Pair<byte[], List<KeyValue>>> uncommittedDataIterator = > > > > PhoenixRuntime.getUncommittedDataIterator(conn, true); > > > > //... > > > > > > > > > > The non-code translation of those steps is: > > > 1. Parse the CSV record > > > 2. Convert the contents of the CSV record into KeyValues > > > > > > Although it may look as though data is being written over the wire to > > > Phoenix, the execution of an upsert executor and retrieval of the > > > uncommitted KeyValues is all local (in memory). The code is implemented > > in > > > this way because JDBC is the general API used within Phoenix -- there > > isn't > > > direct "convert fields to Phoenix encoding" API, although this is doing > > the > > > equivalent operation. > > > > > > Could you give some more information on your performance numbers? For > > > example, is this the throughput that you're getting in a single > process, > > or > > > over a number of processes? If so, how many processes? Also, how many > > > columns are in the records that you're loading? > > > > > > > > > > > > > > We plan to load up-to 100TB of data and overall performance of the > > > > bulk-loader is not satisfactory. > > > > > > > > > > How many records are in that 100TB? What is the current (projected) > time > > > required to load the data? What is the minimum allowable ingest speed > to > > be > > > considered satisfactory? > > > > > > - Gabriel > > > > > >
