The approach you are currently taking seems like the best one based on my
experiences. I believe that writing locally chronological batches with few
series per batch is faster than writing globally chronological batches with
many series in each batch.

On Mon, Jul 11, 2016 at 6:37 AM, Ryan January <[email protected]> wrote:

> Thanks Sean, I appreciate your help.
>
> I did write a quick python script as a proof of concept yesterday, and it
> seems to work ok.  I was a little surprised by how long this approach will
> take, but I haven't yet profiled the code to see where my time is spent.
> Before I go much further I'd like to verify exactly how data is stored on
> disk and how the data is sharded.
>
> During this migration I'm consolidating a number of measurements, with
> part of the metric name becoming an additional tag.  The influxdb-python
> result set returns query results as a list of series.  Due to that
> structure I am writing batches of points in chronological order, but
> repeating that same chronological period roughly 50 times across different
> series within the measurement.  Is this a valid approach, or should I
> interleave the points chronologically, only passing over the same time
> period once?
>
>
> On Jul 10, 2016, at 9:52 PM, Sean Beckett <[email protected]> wrote:
>
> When writing older data, as long as you submit batches of points with
> timestamps in chronological order, it should still be fairly performant.
> Backfilling the downsampling with the INTO clause should also be performant
> provided you restrict the time ranges to a perhaps a week at a time.
>
> We have an internal tool for querying data and replaying it into another
> instance. I'll see if that's something we can release, or if we can point
> you to the right code.
>
> On Sat, Jul 9, 2016 at 4:20 PM, Ryan January <[email protected]> wrote:
>
>> I currently have data I'm trying to migrate from one InfluxDB instance to
>> a newer installation and not sure of the proper way to do so.
>> As a bit of background: We're sending app metrics to statsd, which are
>> passing through telegraf to be persisted in InfluxDB on 10 second
>> intervals. During the migration to the new server I'm taking the
>> opportunity to restructure the schema to account for some shortcomings
>> during the first install. Due to this restructuring I can't perform a
>> simple backup/restore.
>>
>> The old server is .11, new is currently using .13, both using RHEL
>>
>> I'm writing a fairly consistent 20 points per second (total) across 50
>> measurements, each having roughly 130 series.
>> Each measurement has 3 tags and 6 fields.
>> No retention policy is currently in place.
>> The database is approximately 2 gb and covers a timespan of roughly 1
>> year.
>>
>> The new server will store 10s resolution in a 60 day retention policy,
>> the data will also be downsampled to 1m resolution into a 1 year retention
>> policy. I'd like to migrate the current 10s samples into the new server,
>> and 1 year retention policy.
>> I'm planning to backfill the old data after the apps have begun sending
>> metrics to the new server.
>>
>> My current plan is to write a script to query 1 minute aggregate data
>> from the old DB in 1 hour chunks, rewriting that data to the new DB.
>> The little I know about the TSM engine is from videos posted on the
>> influxdata site.  It stated that InfluxDB was optimized around the thought
>> that we're only modifying recent data, and that there may be a write
>> penalty as older shards are rewritten.
>>
>> How much of an issue is this if no data currently exists in those
>> timeframes? Are there other methods of backfill that I should consider?
>>
>> Thank you,
>> Ryan
>>
>>
>>
>>
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>
>
>
> --
> Sean Beckett
> Director of Support and Professional Services
> InfluxDB
>
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-- 
Sean Beckett
Director of Support and Professional Services
InfluxDB

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