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https://issues.apache.org/jira/browse/CASSANDRA-9259?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15238992#comment-15238992
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vincent.poncet edited comment on CASSANDRA-9259 at 4/13/16 10:15 AM:
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In datawarehouse / analytics usecases, you are doing mostly full scans,
(hopefully with some predicate pushdown either projection and filtering), but
it is reading a big numbers of rows, then doing aggregations.
I just want to say that that takes time. So by definition, an analytic query on
an OLTP database is always "wrong", in the sense of at the same time of doing
the query, data changed, deleted, updated, inserted. So operational analytics
is always approximate.
The only way to have exact result of analytic query on an OLTP database would
be to have capabilities to query data during a snapshot to have coherent data
and not being impacted by the data changes during the running of the analytic
query. That's like MVCC in RDBMS. that's has performance cost and is relaxed
for most of analytics workloads.
So, my point is in operational analytics, CL=1 will be perfectly fine.
was (Author: [email protected]):
In datawarehouse / analytics usecases, you are doing mostly full scans,
(hopefully with some predicate pushdown either projection and filtering), but
it is reading a big numbers of rows, then doing aggregations.
I just want to say that that takes time. So by definition, an analytic query on
an OLTP database is always "wrong", in the sense of at the same time of doing
the query, data changed, deleted, updated, inserted. So operational analytics
is always approximate.
So, my point is in operational analytics, CL=1 will be perfectly fine.
> 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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