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From Michael Stolz <mst...@pivotal.io>
Subject Re: Geode for time series data
Date Mon, 22 Feb 2016 15:44:36 GMT
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 <crudbug@gmail.com> 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 <mstolz@pivotal.io> 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 <gchase@pivotal.io>
>> 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 <andrew@nmedia.net> 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/
>>>
>>>
>>
>

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