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From Benjamin Kim <bbuil...@gmail.com>
Subject Re: Performance Question
Date Wed, 29 Jun 2016 18:32:20 GMT
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 <todd@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 <bbuild11@gmail.com <mailto:bbuild11@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 <dan@cloudera.com <mailto:dan@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 <bbuild11@gmail.com <mailto:bbuild11@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 <todd@cloudera.com <mailto:todd@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" <bbuild11@gmail.com <mailto:bbuild11@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 <todd@cloudera.com <mailto:todd@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" <bbuild11@gmail.com <mailto:bbuild11@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 <todd@cloudera.com <mailto:todd@cloudera.com>>
wrote:
>>>>> 
>>>>> On Sat, May 28, 2016 at 7:12 AM, Benjamin Kim <bbuild11@gmail.com
<mailto:bbuild11@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 <todd@cloudera.com <mailto:todd@cloudera.com>>
wrote:
>>>>>> 
>>>>>> On Fri, May 27, 2016 at 8:20 PM, Benjamin Kim <bbuild11@gmail.com
<mailto:bbuild11@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 <mpercy@cloudera.com
<mailto:mpercy@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 <bbuild11@gmail.com
<mailto:bbuild11@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


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