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From Mark Heppner <>
Subject Re: Moving column family into new table
Date Thu, 19 Jan 2017 17:30:31 GMT
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 (
> for_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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