How does that work when you're appending incoming data in realtime? Say
you're getting 1,000,000 data points per day on each of 1,000 incoming
stock symbols. That is 1bln data points. Are you using keys like this
that bucket the data into one array per minute of the day
MSFT-08:00
MSFT-08:01
...
MSFT-08:59
etc?
each array might have several thousand elements in that case.
Thanks
Andrew
On Mon, 22 Feb 2016, Michael Stolz wrote:
> You will definitely want to use arrays rather than storing each individual
> data point because the overhead of each entry in Geode is nearly 300 bytes.
> You could choose to partition by day/week/month but it shouldn't be necessary
> because the default partitioning scheme should be random enough to get
> reasonable distribution
> if you are using the metadata and starting timestamp of the array as the key.
>
>
> --Mike Stolz
> Principal Engineer, GemFire Product Manager
> Mobile: 631-835-4771
>
> On Fri, Feb 19, 2016 at 1:43 PM, Alan Kash <[email protected]> wrote:
> Hi,
> I am also building a dashboard prototype for time-series data,
>
> For time-series data, usually we target a single metric change (stock price,
> temperature, pressure, etc.) for an entity, but the associated metadata with
> event -
> {StockName/Place, DeviceID, ApplicationID, EventType} remains constant.
>
> For a backend like Cassandra, we denormalize everything and put everything in
> a flat key-map with [Metric, Timestamp, DeviceID, Type] as the key. This
> results in data
> duplication of the associated "Metadata".
>
> Do you recommend similar approach for Geode ?
>
> Alternatively,
>
> We can have an array for Metrics associated with a given Metadata key and
> store it in a Map ?
>
> Key = [Metadata, Timestamp]
>
> TSMAP<Key, Array<Metric>> series = [1,2,3,4,5,6,7,8,9]
>
> We can partition this at application level by day / week / month.
>
> Is this approach better ?
>
> There is a metrics spec for TS data modeling for those who are interested -
> http://metrics20.org
>
> Thanks
>
>
>
> On Fri, Feb 19, 2016 at 1:11 PM, Michael Stolz <[email protected]> wrote:
> You will likely get best results in terms of speed of access if you put
> some structure around the way you store the data in-memory.
> First off, you would probably want to parse the data into the individual
> fields and create a Java object that represents that structure.
>
> Then you would probably want to bundle those Java structures into arrays in
> such a way that it is easy to get to the array for a particular date and time
> by the
> combination of a ticker and a date and time as the key.
>
> Those arrays of Java objects is what you would store as entries in Geode.
> I think this would give you the fastest access to the data.
>
> By the way, probably better to use an integer Julian date and a long integer
> for the time rather than a Java Date. Java Dates in Geode PDX are way bigger
> than you
> want when you have millions of them.
>
> Looking at the sample dataset you provided it appears there is a lot of
> redundant data in there. Repeating 1926.75 for instance.
> In fact, every field but 2 are all the same. Are the repetitious fields
> necessary? If they are, then you might consider using a columnar approach
> instead of the
> Java structures I mentioned. Make an array for each column and compact the
> repetitions with a count. It would be slower but more compact.
> The timestamps are all the same too. Strange.
>
>
>
> --Mike Stolz
> Principal Engineer, GemFire Product Manager
> Mobile: 631-835-4771
>
> On Fri, Feb 19, 2016 at 12:15 AM, Gregory Chase <[email protected]> wrote:
> Hi Andrew,I'll let one of the committers answer to your specific data
> file question. However, you might find some inspiration in this open source
> demo
> that some of the Geode team presented at OSCON earlier this year:
> http://pivotal-open-source-hub.github.io/StockInference-Spark/
>
> This was based on a pre-release version of Geode, so you'll want to sub the
> M1 release in and see if any other tweaks are required at that point.
>
> I believe this video and presentation go with the Github project:
> http://www.infoq.com/presentations/r-gemfire-spring-xd
>
> On Thu, Feb 18, 2016 at 8:58 PM, Andrew Munn <[email protected]> wrote:
> What would be the best way to use Geode (or GF) to store and utilize
> financial time series data like a stream of stock trades? I have ASCII
> files with timestamps that include microseconds:
>
> 2016-02-17 18:00:00.000660,1926.75,5,5,1926.75,1926.75,14644971,C,43,01,
> 2016-02-17
> 18:00:00.000660,1926.75,80,85,1926.75,1926.75,14644971,C,43,01,
> 2016-02-17
> 18:00:00.000660,1926.75,1,86,1926.75,1926.75,14644971,C,43,01,
> 2016-02-17
> 18:00:00.000660,1926.75,6,92,1926.75,1926.75,14644971,C,43,01,
> 2016-02-17
> 18:00:00.000660,1926.75,27,119,1926.75,1926.75,14644971,C,43,01,
> 2016-02-17
> 18:00:00.000660,1926.75,3,122,1926.75,1926.75,14644971,C,43,01,
> 2016-02-17
> 18:00:00.000660,1926.75,5,127,1926.75,1926.75,14644971,C,43,01,
> 2016-02-17
> 18:00:00.000660,1926.75,4,131,1926.75,1926.75,14644971,C,43,01,
> 2016-02-17
> 18:00:00.000660,1926.75,2,133,1926.75,1926.75,14644971,C,43,01,
>
> I have one file per day and each file can have over 1,000,000 rows. My
> thought is to fault in the files and parse the ASCII as needed. I know
> I
> could store the data as binary primitives in a file on disk instead of
> ASCII for a bit more speed.
>
> I don't have a cluster of machines to create an HDFS cluster with. My
> machine does have 128GB of RAM though.
>
> Thanks!
>
>
>
>
> --
> Greg Chase
> Global Head, Big Data Communities
> http://www.pivotal.io/big-data
>
> Pivotal Software
> http://www.pivotal.io/
>
> 650-215-0477
> @GregChase
> Blog: http://geekmarketing.biz/
>
>
>
>
>
>
