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From "Stefania (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (CASSANDRA-9259) Bulk Reading from Cassandra
Date Wed, 06 Apr 2016 09:57:27 GMT

    [ https://issues.apache.org/jira/browse/CASSANDRA-9259?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15228054#comment-15228054
] 

Stefania commented on CASSANDRA-9259:
-------------------------------------

Here are the results of a proof of concept of an *optimized read path* for local reads at
CL 1 and *streaming*.

These are the results in *ops/second*:

||Partition Size||Page size||Num. Partitions in the table||Synchronous, no optimization ops/s||Synchronous,
with optimization ops/s||Prefetch a page, no optimization ops/s||Prefetch a page, with optimization
ops/s||Streaming, measurement 1 ops/s||Streaming, measurement 2 ops/s||Improvement due to
local read optimization||Improvement due to streaming||Total improvement||Link to the test||
|100 KBYTES|10|250K|98|148|189|283|784|767|49.74%|174.03%|310.32%|[link|http://cstar.datastax.com/tests/id/6dc252e8-fb0c-11e5-9ad6-0256e416528f]|
|10 KBYTES|100|1M|93|138|174|259|659|673|48.85%|157.14%|282.76%|[link|http://cstar.datastax.com/tests/id/d8002fe2-fad3-11e5-a500-0256e416528f]|
|1 KBYTE|1000|1M|84|133|114|179|233|239|57.02%|31.84%|107.02%|[link|http://cstar.datastax.com/tests/id/8dd1c5ba-fad9-11e5-82e5-0256e416528f]|
|1 KBYTE|1000|2M|60|98|94|153|247|248|62.77%|61.76%|163.30%|[link|http://cstar.datastax.com/tests/id/97be0520-fba6-11e5-bba8-0256e416528f]|
|100 BYTES|10000|5M|21|33|24|37|41|44|54.17%|14.86%|77.08%|[link|http://cstar.datastax.com/tests/id/95e31c0c-fb12-11e5-9ad6-0256e416528f]|
|50 BYTES|20000|5M|20|32|20|33|35|37|65.00%|9.09%|80.00%|[link|http://cstar.datastax.com/tests/id/bf1a52a0-fb8c-11e5-838f-0256e416528f]|
|10 KBYTES|5000|500K|31|46|31|46|45|46|48.39%|-1.09%|46.77%|[link|http://cstar.datastax.com/tests/id/df9e6e96-fbbc-11e5-bf65-0256e416528f]|
|1 KBYTE|5000|2M|35|56|40|64|66|66|60.00%|3.13%|65.00%|[link|http://cstar.datastax.com/tests/id/1d1785fc-fbb6-11e5-bf65-0256e416528f]|
|100 BYTES|5000|5M|22|40|31|53|66|66|70.97%|24.53%|112.90%|[link|http://cstar.datastax.com/tests/id/70262d52-fbac-11e5-a876-0256e416528f]|


These are the same results but expressed in *rows/second*:

||Partition Size||Page size||Num. Partitions in the table||Synchronous, no optimization rows/s||Synchronous,
with optimization rows/s||Prefetch a page, no optimization rows/s||Prefetch a page, with optimization
rows/s||Streaming, measurement 1 rows/s||Streaming, measurement 2 rows/s||Improvement due
to local read optimization||Improvement due to streaming||Total improvement||Link to the test||
|100 KBYTES|10|250K|963|1453|1849|2761|7702|7522|49.32%|175.70%|311.68%|[link|http://cstar.datastax.com/tests/id/6dc252e8-fb0c-11e5-9ad6-0256e416528f]|
|10 KBYTES|100|1M|8830|13159|16572|24591|62649|63975|48.39%|157.46%|282.04%|[link|http://cstar.datastax.com/tests/id/d8002fe2-fad3-11e5-a500-0256e416528f]|
|1 KBYTE|1000|1M|52543|83548|71277|112558|145637|150070|57.92%|31.36%|107.44%|[link|http://cstar.datastax.com/tests/id/8dd1c5ba-fad9-11e5-82e5-0256e416528f]|
|1 KBYTE|1000|2M|47029|76292|74131|119727|193738|193495|61.51%|61.71%|161.18%|[link|http://cstar.datastax.com/tests/id/97be0520-fba6-11e5-bba8-0256e416528f]|
|100 BYTES|10000|5M|88130|142800|100590|158780|176699|185292|57.85%|13.99%|79.93%|[link|http://cstar.datastax.com/tests/id/95e31c0c-fb12-11e5-9ad6-0256e416528f]|
|50 BYTES|20000|5M|94581|153296|97599|157463|169226|175581|61.34%|9.49%|76.64%|[link|http://cstar.datastax.com/tests/id/bf1a52a0-fb8c-11e5-838f-0256e416528f]|
|10 KBYTES|5000|500K|14938|22356|15152|22623|22174|22419|49.31%|-1.44%|47.15%|[link|http://cstar.datastax.com/tests/id/df9e6e96-fbbc-11e5-bf65-0256e416528f]|
|1 KBYTE|5000|2M|63552|100974|71644|115001|119537|119079|60.52%|3.75%|66.53%|[link|http://cstar.datastax.com/tests/id/1d1785fc-fbb6-11e5-bf65-0256e416528f]|
|100 BYTES|5000|5M|70154|126547|98331|168121|208226|207482|70.97%|23.63%|111.38%|[link|http://cstar.datastax.com/tests/id/70262d52-fbac-11e5-a876-0256e416528f]|


The columns above refer to the following cassandra-stress operations:

*Synchronous page retrieval*
The client retrieves each page synchronously, with and without the optimized local read path.

*Asynchronous page retrieval (prefetch)*
The client retrieves the first page synchronously and then prefetches the next page, before
processing the results of the previous page, with and without the optimized local read path.

*Streaming*
The client requests all pages initially and then waits synchronously for the first page. For
the following pages, each operation processes a page that was previously delivered, blocking
only if a page is unavailable. 

There are two equivalent measurements for streaming because the local read path optimization
is always available; it would have added considerable extra work to implement streaming without
optimized read path, and it would have only provided comparison data which is already available.

*Results*
The improvement due to local read optimization is calculated by comparing the asynchronous
(prefetch) result, with and without optimization. The increase is between 50% and 70%.

The improvement due to streaming is calculated by comparing the average of the streaming measurements
with the optimized prefetch results. It varies from negligible change to an additional increase
of 175%, depending on page and row sizes. The reason for this variability is that each cassandra
stress operation iterates over the rows in the page. The smaller the data to iterate over,
the quicker the operation. It is therefore difficult to quantify the overall benefit of streaming
as it depends on the speed at which the client can process results. However, because clients
can always process results asynchronously, streaming should lead in most cases to a material
improvement in overall processing times.

The total improvement is calculated by comparing the average of the streaming measurements
with the unoptimized prefetch result (which is the best rate achievable at present, without
considering any optimizations performed by this patch) and it follows from the two factors
just discussed.


*Test environment*
The tests were performed on [cstart.datastax.com|http://cstar.datastax.com] using the "Taylor"
cluster and the links are available in the tables above. The operation names displayed in
the graphs could not be changed, so please use the following conversions when viewing results:
{{1_user=insert, 2_user=streaming, 3_user=prefetch, 4_user=synchronous retrieval}}

*Steps forward*
In view of these results, I believe an optimized local read path is worth pursuing whilst
streaming should also provide benefits to asynchronous clients. Comments are welcome.



> Bulk Reading from Cassandra
> ---------------------------
>
>                 Key: CASSANDRA-9259
>                 URL: https://issues.apache.org/jira/browse/CASSANDRA-9259
>             Project: Cassandra
>          Issue Type: New Feature
>          Components: Compaction, CQL, Local Write-Read Paths, Streaming and Messaging,
Testing
>            Reporter:  Brian Hess
>            Assignee: Stefania
>            Priority: Critical
>             Fix For: 3.x
>
>         Attachments: bulk-read-benchmark.1.html, bulk-read-jfr-profiles.1.tar.gz, bulk-read-jfr-profiles.2.tar.gz
>
>
> This ticket is following on from the 2015 NGCC.  This ticket is designed to be a place
for discussing and designing an approach to bulk reading.
> The goal is to have a bulk reading path for Cassandra.  That is, a path optimized to
grab a large portion of the data for a table (potentially all of it).  This is a core element
in the Spark integration with Cassandra, and the speed at which Cassandra can deliver bulk
data to Spark is limiting the performance of Spark-plus-Cassandra operations.  This is especially
of importance as Cassandra will (likely) leverage Spark for internal operations (for example
CASSANDRA-8234).
> The core CQL to consider is the following:
> SELECT a, b, c FROM myKs.myTable WHERE Token(partitionKey) > X AND Token(partitionKey)
<= Y
> Here, we choose X and Y to be contained within one token range (perhaps considering the
primary range of a node without vnodes, for example).  This query pushes 50K-100K rows/sec,
which is not very fast if we are doing bulk operations via Spark (or other processing frameworks
- ETL, etc).  There are a few causes (e.g., inefficient paging).
> There are a few approaches that could be considered.  First, we consider a new "Streaming
Compaction" approach.  The key observation here is that a bulk read from Cassandra is a lot
like a major compaction, though instead of outputting a new SSTable we would output CQL rows
to a stream/socket/etc.  This would be similar to a CompactionTask, but would strip out some
unnecessary things in there (e.g., some of the indexing, etc). Predicates and projections
could also be encapsulated in this new "StreamingCompactionTask", for example.
> Another approach would be an alternate storage format.  For example, we might employ
Parquet (just as an example) to store the same data as in the primary Cassandra storage (aka
SSTables).  This is akin to Global Indexes (an alternate storage of the same data optimized
for a particular query).  Then, Cassandra can choose to leverage this alternate storage for
particular CQL queries (e.g., range scans).
> These are just 2 suggestions to get the conversation going.
> One thing to note is that it will be useful to have this storage segregated by token
range so that when you extract via these mechanisms you do not get replications-factor numbers
of copies of the data.  That will certainly be an issue for some Spark operations (e.g., counting).
 Thus, we will want per-token-range storage (even for single disks), so this will likely leverage
CASSANDRA-6696 (though, we'll want to also consider the single disk case).
> It is also worth discussing what the success criteria is here.  It is unlikely to be
as fast as EDW or HDFS performance (though, that is still a good goal), but being within some
percentage of that performance should be set as success.  For example, 2x as long as doing
bulk operations on HDFS with similar node count/size/etc.



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