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From Josh Mahonin <>
Subject Re: Moving column family into new table
Date Thu, 19 Jan 2017 18:28:41 GMT
It's a bit peculiar that you've got it pre-split to 10 salt buckets, but
seeing 400+ partitions. It sounds like HBase is splitting the regions on
you, possibly due to the 'hbase.hregion.max.filesize' setting. You should
be able to check the HBase Master UI and see the table details to see how
many regions there are, and what nodes they're located on. Right now, the
Phoenix MR / Spark integration basically assigns one partition per region.

As a total guess, I wonder if somehow the first 380 partitions are
relatively sparse, and the bulk of the data is in the remaining 70
partitions. You might be able to diagnose that by adding some logging in a
'mapPartitions()' call. It's possible that running a major compaction on
that table might help redistribute the data as well.

If you're seeing your task getting killed, definitely try dig into the
Spark executor / driver logs to try find a root cause. If you're using
YARN, you can usually get into the Spark history server, then check the
'stdout' / 'stderr' logs for each executor.

Re: architecture recommendations, it's possible that phoenix-spark isn't
the right tool for this job, though we routinely read / write billions of
rows with it. I'd recommend trying to start with a smaller subset of your
data and make sure you've got the schema, queries and HBase settings setup
the way you like, then add Spark into the mix. Then start adding a bit more
data, check results, find any bottlenecks, and tune as needed.

If you're able to identify any issues specifically with Phoenix, bug
reports and patches are greatly appreciated!

Best of luck,


On Thu, Jan 19, 2017 at 12:30 PM, Mark Heppner <>

> Thanks for the quick reply, Josh!
> For our demo cluster, we have 5 nodes, so the table was already set to 10
> salt buckets. I know you can increase the salt buckets after the table is
> created, but how do you change the split points? The repartition in Spark
> seemed to be extremely inefficient, so we were trying to skip it and keep
> the 400+ default partitions.
> The biggest issue we're facing is that as Spark goes through the
> partitions during the scan, it becomes exponentially slower towards the
> end. Around task 380/450, it slows down to a halt, eventually timing out
> around 410 and getting killed. We have no idea if this is something with
> Spark, YARN, or HBase, so that's why we were brainstorming with using the
> foreign key-based layout, hoping that the files on HDFS would be more
> compacted.
> We haven't noticed too much network overhead, nor have we seen CPU or RAM
> usage too high. Our nodes are pretty big, 32 cores and 256 GB RAM each,
> connected on a 10 GbE network. Even if our query is for 80-100 rows, the
> Spark job still slows to a crawl at the end, but that should really only be
> about 80 MB of data it would be pulling out of Phoenix into the executors.
> I guess we should have verified that the Phoenix+Spark plugin did achieve
> data locality, but there isn't anything that says otherwise. Even though it
> doesn't have data locality, we have no idea why it would progressively slow
> down as it reaches the end of the scan/filter.
> The images are converted to a NumPy array, then saved as a binary string
> into Phoenix. In Spark, this is fairly quick to convert the binary string
> back to the NumPy array. This also allows us to use GET_BYTE() from Phoenix
> to extract specific values within the array, without going through Spark at
> all. Do you have any other architecture recommendations for our use case?
> Would storing the images directly in HBase be any better?
> On Thu, Jan 19, 2017 at 12:02 PM, Josh Mahonin <> wrote:
>> Hi Mark,
>> At present, the Spark partitions are basically equivalent to the number
>> of regions in the underlying HBase table. This is typically something you
>> can control yourself, either using pre-splitting or salting (
>> optimizing_Phoenix). Given that you have 450+ partitions though, it
>> sounds like you should be able to achieve a decent level or parallelism,
>> but that's a knob you can fiddle with. It might also be useful to look at
>> Spark's "repartition" operation if you have idle Spark executors.
>> The partitioning is sort of orthogonal from the primary key layout and
>> the resulting query efficiency, but the strategy you've taken with your
>> schema seems fairly sensible to me. Given that your primary key is the 'id'
>> field, the query you're using is going to be much more efficient than,
>> e.g., filtering on the 'title' column. Iterating on your schema and queries
>> using straight SQL and then applying that to Spark after is probably a good
>> strategy here to get more familiar with query performance.
>> If you're reading the binary 'data' column in Spark and seeing a lot of
>> network overhead, one thing to be aware of is the present Phoenix MR /
>> Spark code isn't location aware, so executors are likely reading big chunks
>> of data from another node. There's a few patches in to address this, but
>> they're not in a released version yet:
>> Good luck!
>> Josh
>> On Thu, Jan 19, 2017 at 11:30 AM, Mark Heppner <>
>> wrote:
>>> Our use case is to analyze images using Spark. The images are typically
>>> ~1MB each, so in order to prevent the small files problem in HDFS, we went
>>> with HBase and Phoenix. For 20+ million images and metadata, this has been
>>> working pretty well so far. Since this is pretty new to us, we didn't
>>> create a robust design:
>>> (
>>>     title VARCHAR,
>>>     ...
>>>     image.dtype VARCHAR(12),
>>>     image.width UNSIGNED_INT,
>>>     image.height UNSIGNED_INT,
>>> )
>>> Most queries are on the metadata, so all of that is kept in the default
>>> column family. Only the image data is stored in a secondary column family.
>>> Additional indexes are created anyways, so the main table isn't usually
>>> touched.
>>> We first run a Phoenix query to check if there are any matches. If so,
>>> then we start a Spark job on the images. The primary keys are sent to the
>>> PySpark job, which then grabs the images based on the primary keys:
>>> df = \
>>>     .format('org.apache.phoenix.spark') \
>>>     .option('table', 'mytable') \
>>>     .option('zkUrl', 'localhost:2181:/hbase-unsecure') \
>>>     .load()
>>> df.registerTempTable('mytable')
>>> query =
>>> df_imgs = sqlContext.sql(
>>>     'SELECT IMAGE FROM mytable WHERE ID = 1 OR ID = 2 ...'
>>> )
>>> When this was first designed, we thought since the lookup was by primary
>>> key, it would be smart enough to do a skip scan, but it appears to be doing
>>> a full scan. The df_imgs.rdd.getNumPartitions() ends up being 450+, which
>>> matches up with the number of split files in HDFS.
>>> Would it be better to use a foreign key and split the tables :
>>> (
>>>     title VARCHAR,
>>>     image_id VARCHAR(36)
>>> )
>>> (
>>>     image_id VARCHAR(36) NOT NULL PRIMARY KEY,
>>>     dtype VARCHAR(12),
>>>     width UNSIGNED_INT,
>>>     height UNSIGNED_INT,
>>>     data VARBINARY
>>> )
>>> If the first query grabs the image_ids and send them to Spark, would
>>> Spark be able to handle the query more efficiently?
>>> If this is a better design, is there any way of moving the "image"
>>> column family from "mytable" to the default column family of the new
>>> "images" table? Is it possible to create the new table with the
>>> "image_id"s, make the foreign keys, then move the column family into the
>>> new table?
>>> --
>>> Mark Heppner
> --
> Mark Heppner

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