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From Benjamin Kim <bbuil...@gmail.com>
Subject Re: Performance Question
Date Wed, 15 Jun 2016 16:17:11 GMT
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> 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
>> 
> 


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