Todd, I started Spark streaming more events into Kudu. Performance is great there too! With HBase, it’s fast too, but I noticed that it pauses here and there, making it take seconds for > 40k rows at a time, while Kudu doesn’t. The progress bar just blinks by. I will keep this running until it hits 1B rows and rerun my performance tests. This, hopefully, will give better numbers.
Thanks, Ben > On Jun 28, 2016, at 4:26 PM, Todd Lipcon <t...@cloudera.com> wrote: > > Cool, thanks for the report, Ben. For what it's worth, I think there's still > some low hanging fruit in the Spark connector for Kudu (for example, I > believe locality on reads is currently broken). So, you can expect > performance to continue to improve in future versions. I'd also be interested > to see results on Kudu for a much larger dataset - my guess is a lot of the 6 > seconds you're seeing is constant overhead from Spark job setup, etc, given > that the performance doesn't seem to get slower as you went from 700K rows to > 13M rows. > > -Todd > > On Tue, Jun 28, 2016 at 3:03 PM, Benjamin Kim <bbuil...@gmail.com > <mailto:bbuil...@gmail.com>> wrote: > FYI. > > I did a quick-n-dirty performance test. > > First, the setup: > QA cluster: > 15 data nodes > 64GB memory each > HBase is using 4GB of memory > Kudu is using 1GB of memory > 1 HBase/Kudu master node > 64GB memory > HBase/Kudu master is using 1GB of memory each > 10Gb Ethernet > > Using Spark on both to load/read events data (84 columns per row), I was able > to record performance for each. On the HBase side, I used the Phoenix 4.7 > Spark plugin where DataFrames can be used directly. On the Kudu side, I used > the Spark connector. I created an events table in Phoenix using the CREATE > TABLE statement and created the equivalent in Kudu using the Spark method > based off of a DataFrame schema. > > Here are the numbers for Phoenix/HBase. > 1st run: > > 715k rows > - write: 2.7m > > > 715k rows in HBase table > - read: 0.1s > - count: 3.8s > - aggregate: 61s > > 2nd run: > > 5.2M rows > - write: 11m > * had 4 region servers go down, had to retry the 5.2M row write > > > 5.9M rows in HBase table > - read: 8s > - count: 3m > - aggregate: 46s > > 3rd run: > > 6.8M rows > - write: 9.6m > > > 12.7M rows > - read: 10s > - count: 3m > - aggregate: 44s > > > Here are the numbers for Kudu. > 1st run: > > 715k rows > - write: 18s > > > 715k rows in Kudu table > - read: 0.2s > - count: 18s > - aggregate: 5s > > 2nd run: > > 5.2M rows > - write: 33s > > > 5.9M rows in Kudu table > - read: 0.2s > - count: 16s > - aggregate: 6s > > 3rd run: > > 6.8M rows > - write: 27s > > > 12.7M rows in Kudu table > - read: 0.2s > - count: 16s > - aggregate: 6s > > The Kudu results are impressive if you take these number as-is. Kudu is close > to 18x faster at writing (UPSERT). Kudu is 30x faster at reading (HBase times > increase as data size grows). Kudu is 7x faster at full row counts. Lastly, > Kudu is 3x faster doing an aggregate query (count distinct event_id’s per > user_id). *Remember that this is small cluster, times are still respectable > for both systems, HBase could have been configured better, and the HBase > table could have been better tuned. > > Cheers, > Ben > > >> On Jun 15, 2016, at 10:13 AM, Dan Burkert <d...@cloudera.com >> <mailto:d...@cloudera.com>> wrote: >> >> Adding partition splits when range partitioning is done via the >> CreateTableOptions.addSplitRow >> <http://getkudu.io/apidocs/org/kududb/client/CreateTableOptions.html#addSplitRow-org.kududb.client.PartialRow-> >> method. You can find more about the different partitioning options in the >> schema design guide >> <http://getkudu.io/docs/schema_design.html#data-distribution>. We generally >> recommend sticking to hash partitioning if possible, since you don't have to >> determine your own split rows. >> >> - Dan >> >> On Wed, Jun 15, 2016 at 9:17 AM, Benjamin Kim <bbuil...@gmail.com >> <mailto:bbuil...@gmail.com>> wrote: >> Todd, >> >> I think the locality is not within our setup. We have the compute cluster >> with Spark, YARN, etc. on its own, and we have the storage cluster with >> HBase, Kudu, etc. on another. We beefed up the hardware specs on the compute >> cluster and beefed up storage capacity on the storage cluster. We got this >> setup idea from the Databricks folks. I do have a question. I created the >> table to use range partition on columns. I see that if I use hash partition >> I can set the number of splits, but how do I do that using range (50 nodes * >> 10 = 500 splits)? >> >> Thanks, >> Ben >> >> >>> On Jun 15, 2016, at 9:11 AM, Todd Lipcon <t...@cloudera.com >>> <mailto:t...@cloudera.com>> wrote: >>> >>> Awesome use case. One thing to keep in mind is that spark parallelism will >>> be limited by the number of tablets. So, you might want to split into 10 or >>> so buckets per node to get the best query throughput. >>> >>> Usually if you run top on some machines while running the query you can see >>> if it is fully utilizing the cores. >>> >>> Another known issue right now is that spark locality isn't working properly >>> on replicated tables so you will use a lot of network traffic. For a perf >>> test you might want to try a table with replication count 1 >>> >>> On Jun 15, 2016 5:26 PM, "Benjamin Kim" <bbuil...@gmail.com >>> <mailto:bbuil...@gmail.com>> wrote: >>> Hi Todd, >>> >>> I did a simple test of our ad events. We stream using Spark Streaming >>> directly into HBase, and the Data Analysts/Scientists do some >>> insight/discovery work plus some reports generation. For the reports, we >>> use SQL, and the more deeper stuff, we use Spark. In Spark, our main data >>> currency store of choice is DataFrames. >>> >>> The schema is around 83 columns wide where most are of the string data type. >>> >>> "event_type", "timestamp", "event_valid", "event_subtype", "user_ip", >>> "user_id", "mappable_id", >>> "cookie_status", "profile_status", "user_status", "previous_timestamp", >>> "user_agent", "referer", >>> "host_domain", "uri", "request_elapsed", "browser_languages", "acamp_id", >>> "creative_id", >>> "location_id", “pcamp_id", >>> "pdomain_id", "continent_code", "country", "region", "dma", "city", "zip", >>> "isp", "line_speed", >>> "gender", "year_of_birth", "behaviors_read", "behaviors_written", >>> "key_value_pairs", "acamp_candidates", >>> "tag_format", "optimizer_name", "optimizer_version", "optimizer_ip", >>> "pixel_id", “video_id", >>> "video_network_id", "video_time_watched", "video_percentage_watched", >>> "video_media_type", >>> "video_player_iframed", "video_player_in_view", "video_player_width", >>> "video_player_height", >>> "conversion_valid_sale", "conversion_sale_amount", >>> "conversion_commission_amount", "conversion_step", >>> "conversion_currency", "conversion_attribution", "conversion_offer_id", >>> "custom_info", "frequency", >>> "recency_seconds", "cost", "revenue", “optimizer_acamp_id", >>> "optimizer_creative_id", "optimizer_ecpm", "impression_id", >>> "diagnostic_data", >>> "user_profile_mapping_source", "latitude", "longitude", "area_code", >>> "gmt_offset", "in_dst", >>> "proxy_type", "mobile_carrier", "pop", "hostname", "profile_expires", >>> "timestamp_iso", "reference_id", >>> "identity_organization", "identity_method" >>> >>> Most queries are like counts of how many users use what browser, how many >>> are unique users, etc. The part that scares most users is when it comes to >>> joining this data with other dimension/3rd party events tables because of >>> shear size of it. >>> >>> We do what most companies do, similar to what I saw in earlier >>> presentations of Kudu. We dump data out of HBase into partitioned Parquet >>> tables to make query performance manageable. >>> >>> I will coordinate with a data scientist today to do some tests. He is >>> working on identity matching/record linking of users from 2 domains: US and >>> Singapore, using probabilistic deduping algorithms. I will load the data >>> from ad events from both countries, and let him run his process against >>> this data in Kudu. I hope this will “wow” the team. >>> >>> Thanks, >>> Ben >>> >>>> On Jun 15, 2016, at 12:47 AM, Todd Lipcon <t...@cloudera.com >>>> <mailto:t...@cloudera.com>> wrote: >>>> >>>> Hi Benjamin, >>>> >>>> What workload are you using for benchmarks? Using spark or something more >>>> custom? rdd or data frame or SQL, etc? Maybe you can share the schema and >>>> some queries >>>> >>>> Todd >>>> >>>> Todd >>>> >>>> On Jun 15, 2016 8:10 AM, "Benjamin Kim" <bbuil...@gmail.com >>>> <mailto:bbuil...@gmail.com>> wrote: >>>> Hi Todd, >>>> >>>> Now that Kudu 0.9.0 is out. I have done some tests. Already, I am >>>> impressed. Compared to HBase, read and write performance are better. Write >>>> performance has the greatest improvement (> 4x), while read is > 1.5x. >>>> Albeit, these are only preliminary tests. Do you know of a way to really >>>> do some conclusive tests? I want to see if I can match your results on my >>>> 50 node cluster. >>>> >>>> Thanks, >>>> Ben >>>> >>>>> On May 30, 2016, at 10:33 AM, Todd Lipcon <t...@cloudera.com >>>>> <mailto:t...@cloudera.com>> wrote: >>>>> >>>>> On Sat, May 28, 2016 at 7:12 AM, Benjamin Kim <bbuil...@gmail.com >>>>> <mailto:bbuil...@gmail.com>> wrote: >>>>> Todd, >>>>> >>>>> It sounds like Kudu can possibly top or match those numbers put out by >>>>> Aerospike. Do you have any performance statistics published or any >>>>> instructions as to measure them myself as good way to test? In addition, >>>>> this will be a test using Spark, so should I wait for Kudu version 0.9.0 >>>>> where support will be built in? >>>>> >>>>> We don't have a lot of benchmarks published yet, especially on the write >>>>> side. I've found that thorough cross-system benchmarks are very difficult >>>>> to do fairly and accurately, and often times users end up misguided if >>>>> they pay too much attention to them :) So, given a finite number of >>>>> developers working on Kudu, I think we've tended to spend more time on >>>>> the project itself and less time focusing on "competition". I'm sure >>>>> there are use cases where Kudu will beat out Aerospike, and probably use >>>>> cases where Aerospike will beat Kudu as well. >>>>> >>>>> From my perspective, it would be great if you can share some details of >>>>> your workload, especially if there are some areas you're finding Kudu >>>>> lacking. Maybe we can spot some easy code changes we could make to >>>>> improve performance, or suggest a tuning variable you could change. >>>>> >>>>> -Todd >>>>> >>>>> >>>>>> On May 27, 2016, at 9:19 PM, Todd Lipcon <t...@cloudera.com >>>>>> <mailto:t...@cloudera.com>> wrote: >>>>>> >>>>>> On Fri, May 27, 2016 at 8:20 PM, Benjamin Kim <bbuil...@gmail.com >>>>>> <mailto:bbuil...@gmail.com>> wrote: >>>>>> Hi Mike, >>>>>> >>>>>> First of all, thanks for the link. It looks like an interesting read. I >>>>>> checked that Aerospike is currently at version 3.8.2.3, and in the >>>>>> article, they are evaluating version 3.5.4. The main thing that >>>>>> impressed me was their claim that they can beat Cassandra and HBase by >>>>>> 8x for writing and 25x for reading. Their big claim to fame is that >>>>>> Aerospike can write 1M records per second with only 50 nodes. I wanted >>>>>> to see if this is real. >>>>>> >>>>>> 1M records per second on 50 nodes is pretty doable by Kudu as well, >>>>>> depending on the size of your records and the insertion order. I've been >>>>>> playing with a ~70 node cluster recently and seen 1M+ writes/second >>>>>> sustained, and bursting above 4M. These are 1KB rows with 11 columns, >>>>>> and with pretty old HDD-only nodes. I think newer flash-based nodes >>>>>> could do better. >>>>>> >>>>>> >>>>>> To answer your questions, we have a DMP with user profiles with many >>>>>> attributes. We create segmentation information off of these attributes >>>>>> to classify them. Then, we can target advertising appropriately for our >>>>>> sales department. Much of the data processing is for applying models on >>>>>> all or if not most of every profile’s attributes to find similarities >>>>>> (nearest neighbor/clustering) over a large number of rows when batch >>>>>> processing or a small subset of rows for quick online scoring. So, our >>>>>> use case is a typical advanced analytics scenario. We have tried HBase, >>>>>> but it doesn’t work well for these types of analytics. >>>>>> >>>>>> I read, that Aerospike in the release notes, they did do many >>>>>> improvements for batch and scan operations. >>>>>> >>>>>> I wonder what your thoughts are for using Kudu for this. >>>>>> >>>>>> Sounds like a good Kudu use case to me. I've heard great things about >>>>>> Aerospike for the low latency random access portion, but I've also heard >>>>>> that it's _very_ expensive, and not particularly suited to the columnar >>>>>> scan workload. Lastly, I think the Apache license of Kudu is much more >>>>>> appealing than the AGPL3 used by Aerospike. But, that's not really a >>>>>> direct answer to the performance question :) >>>>>> >>>>>> >>>>>> Thanks, >>>>>> Ben >>>>>> >>>>>> >>>>>>> On May 27, 2016, at 6:21 PM, Mike Percy <mpe...@cloudera.com >>>>>>> <mailto:mpe...@cloudera.com>> wrote: >>>>>>> >>>>>>> Have you considered whether you have a scan heavy or a random access >>>>>>> heavy workload? Have you considered whether you always access / update >>>>>>> a whole row vs only a partial row? Kudu is a column store so has some >>>>>>> awesome performance characteristics when you are doing a lot of >>>>>>> scanning of just a couple of columns. >>>>>>> >>>>>>> I don't know the answer to your question but if your concern is >>>>>>> performance then I would be interested in seeing comparisons from a >>>>>>> perf perspective on certain workloads. >>>>>>> >>>>>>> Finally, a year ago Aerospike did quite poorly in a Jepsen test: >>>>>>> https://aphyr.com/posts/324-jepsen-aerospike >>>>>>> <https://aphyr.com/posts/324-jepsen-aerospike> >>>>>>> >>>>>>> I wonder if they have addressed any of those issues. >>>>>>> >>>>>>> Mike >>>>>>> >>>>>>> On Friday, May 27, 2016, Benjamin Kim <bbuil...@gmail.com >>>>>>> <mailto:bbuil...@gmail.com>> wrote: >>>>>>> I am just curious. How will Kudu compare with Aerospike >>>>>>> (http://www.aerospike.com <http://www.aerospike.com/>)? I went to a >>>>>>> Spark Roadshow and found out about this piece of software. It appears >>>>>>> to fit our use case perfectly since we are an ad-tech company trying to >>>>>>> leverage our user profiles data. Plus, it already has a Spark connector >>>>>>> and has a SQL-like client. The tables can be accessed using Spark SQL >>>>>>> DataFrames and, also, made into SQL tables for direct use with Spark >>>>>>> SQL ODBC/JDBC Thriftserver. I see from the work done here >>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/ >>>>>>> <http://gerrit.cloudera.org:8080/#/c/2992/> that the Spark integration >>>>>>> is well underway and, from the looks of it lately, almost complete. I >>>>>>> would prefer to use Kudu since we are already a Cloudera shop, and Kudu >>>>>>> is easy to deploy and configure using Cloudera Manager. I also hope >>>>>>> that some of Aerospike’s speed optimization techniques can make it into >>>>>>> Kudu in the future, if they have not been already thought of or >>>>>>> included. >>>>>>> >>>>>>> Just some thoughts… >>>>>>> >>>>>>> Cheers, >>>>>>> Ben >>>>>>> >>>>>>> >>>>>>> -- >>>>>>> -- >>>>>>> Mike Percy >>>>>>> Software Engineer, Cloudera >>>>>>> >>>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> Todd Lipcon >>>>>> Software Engineer, Cloudera >>>>> >>>>> >>>>> >>>>> >>>>> -- >>>>> Todd Lipcon >>>>> Software Engineer, Cloudera >>>> >>> >> >> > > > > > -- > Todd Lipcon > Software Engineer, Cloudera
