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From <>
Subject RE: Performance migrating from MySQL to C*
Date Wed, 28 May 2014 16:23:07 GMT
Just looking at the data modeling issue:

Your queries seem to always be for a single dataName. So, that should be the main part of
the row key.
Within that, it seems you need to be able to select a range based on time. So, time should
be the primary sort key for the column name.

Based on those requirements, I’d suggest you define the table as:
row key: dataName, dayRange, discriminator
column name: time, sensorId
column value: dataValue

As you can see, I’ve added a couple of fields to the row key:

·         dayRange: to prevent all the values for dataRange from forming one monstrous row,
break it up in chunks of X days. Set X too small and you’ll have to perform a lot of row
queries to answer queries about months or years. Set X too large and you’ll have to do too
much I/O for queries that require only a day of info. I suggest X=5

·         discriminator: To prevent hot spots. If all your writes for a given dataType over
a 5 day period all go to the same C* node, you have a hot spot. To prevent this, add this
discriminator field, and increment it for every write, modulo the number of C* nodes in your
cluster. (See for a much
better explanation of this.)

In CQL terms, I believe it would look like this:

CREATE TABLE sensorData (
            dataName TEXT,
            dayRange int,
            discriminator int,
            time TIMESTAMP,
            sensorId bigint,
            dataValue DOUBLE,
            PRIMARY KEY ((dataName, dayRange, discriminator), time, sensorId)

Hope this helps.

From: Simon Chemouil []
Sent: Wednesday, May 28, 2014 6:26 PM
Subject: Performance migrating from MySQL to C*


First, sorry for the length of this mail. TL;DR: DataModeling timeseries with an extra dimension,
and C* not handling stress well; MySQL doesn't scale as well but handles the queries way better
on similar hardware.


We've been evaluating Cassandra for a while now (~1 month) as a replacement of our current
MySQL based solution. While we're very interested in the scalability promises of Cassandra,
the results we had so far are not as good as we expected.

Our system is providing almost real-time analytics on our (quite large, but definitely not
'Big data') dataset, and we are beginning to wonder if Cassandra is the right tool or if we're
simply doing something wrong. We've spent a lot of effort trying to validate our usage of
C* internally so I would appreciate any pointers or ideas.

I have read that Cassandra was not so good when it cames to reads, or that it was more suited
to returning smaller datasets, but I've also noticed it is being more and more used and advertised
as a Big Data solution (e.g the recent partnership between DataBricks and DataStax).

The problem we try to model is so: we have sensors (millions of them) of different types (thousands
of them), that each report many pieces of data (typed double) every 5 minutes (00:00, 00:05,
00:10, ..., 23:50, 23:55). That's about 735K timestamped values per year per data, per sensor.

We want to be able, for instance, to compute the average value for a given piece of data and
a given set of sensors over a month as fast as possible.


Cassandra 2.0.7, on a 32-cores Linux 64 machine, using XFS and 4TB SSDs with 128 GB of RAM.
DataStax Java Driver 2.0.2 with -Xmx16G. All queries using PreparedStatements.

Data Model:

We've tried several data models for this:
CREATE TABLE sensorData (
            day timestamp,
            sensorId bigint,
            time timestamp,
            values map<text, double>,
            PRIMARY KEY ((day, sensorId), time)

In this model, we cram all the data gathered by a single sensor into a map, so that we can
perform computations on-the-fly when we get the data. The problem is that we sometime have
up to 10K values stored while we'd like to retrieve only 10, and Cassandra is not only unable
to let us select the map keys we're interested in, it is also unable to partially read that
cell... and it makes these queries slow.

Instead we've moved towards storing each value in different tables, with this model:

CREATE TABLE sensorData (
            sensorId bigint,
            time TIMESTAMP,
            dataName TEXT,
            dataValue DOUBLE,
            PRIMARY KEY ((dataName, sensorId), time)

Now, we have to synchronize the time field client-side, which is a bit costly but at least
we only get the data we need. We removed the day component (which was used to further partition
the data) and put the dataName instead.

We've also tried changing the compaction strategy (to LeveledCompactionStrategy), removing
the compression, and generally tweaking our tables without any noticeable gain.

Do these models seem OK for our purpose? They work fine when working with a few hundred sensors,
but how can we query 300K sensorIds without killing Cassandra?

I tried adding a secondary index on an extra-field (sensorTypeId) to get everything and filter
client-side, but then we lose the ability to slice on the time. I tried introducing an extra
info in the table name itself (e.g sensorData_<day>) but not only it is ugly, but it
also increases the number of queries we have to send by the number of days we query, and the
amount of queries we send already seems too high for Cassandra.

Query volume:

We want our queries to span from few sensorIds to hundred thousands of them. We issue queries
such as:
SELECT * FROM sensorData WHERE dataName = 'yyy' AND sensorID IN (<list>) AND time >=
<startTime> AND time <= <endTime>;

We are extremely limited in the size of our list. I read that IN queries were not meant for
large sets, but if we issue several queries with smallers sets we often end-up with the same
situation: timeout exceptions in the Java driver and quite often dead Cassandra nodes.

These are the kind of exceptions we often get:

Exception in thread "Thread-4029" Exception in thread "Thread-3972" com.datastax.driver.core.exceptions.NoHostAvailableException:
All host(s) tried for query failed (tried: [/<>,
/<>, /<>,
/<>] - use getErrors() for details)
            at com.datastax.driver.core.exceptions.NoHostAvailableException.copy(
            at com.datastax.driver.core.DefaultResultSetFuture.extractCauseFromExecutionException(
            at com.datastax.driver.core.DefaultResultSetFuture.getUninterruptibly(
            at com.datastax.driver.core.SessionManager.execute(
            at com.davfx.cassandra.TestClient$
Caused by: com.datastax.driver.core.exceptions.NoHostAvailableException: All host(s) tried
for query failed (tried: /<> (com.datastax.driver.core.exceptions.DriverException:
Timeout during read), /<> (com.datastax.driver.core.exceptions.DriverException:
Timeout during read))
            at com.datastax.driver.core.RequestHandler.sendRequest(
            at com.datastax.driver.core.RequestHandler$
            at java.util.concurrent.ThreadPoolExecutor.runWorker(
            at java.util.concurrent.ThreadPoolExecutor$
            ... 1 more

(before starting the query, Cassandra is running fine and showing up as healthy in OpsCenter;
smaller queries run fine. getErrors() doesn't provide so much info).

I get that these are timeouts, but is changing the timeout settings in cassandra.yaml the
right solution? It feels hackish. Other times, Cassandra nodes just 'die' and we get the sensation
that Cassandra is a bit 'fragile' (we broke our cluster several times during our tests, by
sending too many queries concurrently or simply by creating a table just after dropping it).
Is there any recommended way to avoid stressing too much Cassandra, without manually keeping
track of ongoing queries client-side?

I noticed several JIRA issues that could probably help us reduce the overhead we have (either
by having useless data transfered over the wire, or requiring us to flood SELECT queries to
Cassandra) by allowing multiple IN queries (CASSANDRA-4762), or filtering server-side (CASSANDRA-4914,
CASSANDRA-6377), but they haven't been updated in a while.


Finally, when we try to avoid stressing our cluster too much, we manage to run our queries
('our' queries translate to several Cassandra queries). However, where running the same queries
on MySQL will take only one CPU core to 100%, Cassandra takes our 32 cores to 100% and doesn't
reply any faster than MySQL. In practice we've found MySQL to be able to concurrently run
several queries, also suffering a performance loss but not to the extent of Cassandra.

We're looking at Cassandra today because we know that its scaling capability is very superior
to MySQL's. We know that adding servers will help us increase throughput dramatically, but
we also must be able to keep decent performance on a setup similar to what we're currently
running. We are thus facing several problems:
* it is a very hard sell if the performance is too far from MySQL's (comparing time performance,
and ability to handle the load of several concurrent queries). We're not comparing on a single
server, because we know MySQL has been around longer and is very optimized at what it does,
but we'd at least expect Cassandra to do with 3 servers as good as MySQL does with 2. We've
been unable to demonstrate that so far :(.
* when we stress Cassandra, we get timeouts, very high loads and even make the process become
unresponsive (doesn't necessarily 'crash')... But since we are limited in the queries we can
express, we have no choice but to split them into many smaller queries (that would be written
in a single SQL query) which seems to be a significant overhead. This is probably also a misuse
from our side (even though we're simply using the DataStax Java driver and sending a few queries
with probably too many elemets in the IN relation on the last component of the partition key).
Is there any recommended (preferrably built-in) way to let Cassandra breathe while sending
our queries so we're not crashing it?
* our problem is two-dimensional ... we query on a given time range, but also on a bunch of
sensorIds (up to 500K). It is a difficult problem generally, and we try to pre-compute as
much as we can to denormalize (e.g give an identifier to a sensor-set frequently used), but
our queries are very dynamic and we can only do so much. While most NoSQL datastores don't
seem to have any smarter solution for this, we've found that MySQL does pretty good (by using
different indices or filtering server-side). How to model it best with Cassandra to keep its
strengths? Can we expect improvements in C* to help us deal with this kind of query?

Which finally brings us to the more important question: do you feel Cassandra is fit for our
use-case? I've seen Cassandra being advertised as a 'Big data' solution a lot (and we're working
with smaller datasets) so I'd expect it to be more resilient to stressful usage and more feature-complete
when it comes to reading large datasets... Maybe I have overlooked some pieces of documentation.
We would be OK to try to adjust Cassandra to fit our needs and contribute to the project,
but we have to make sure that the project is going in a similar direction (big data, etc).

Thanks for your help, comments are greatly appreciated.



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