Thread: Partioning with overlapping and non overlapping constraints
Perhaps, I do not fully understand completely, but would it not be simpler to just rearrange the key (and partition) by date & location?
EG: 2015_01_01_metrics_location_XXXXX
In that way, you would only have 365 partitions per year at most. But you also have the option to break it down by week or month, or year.
EG:
EXTRACT(YEAR FROM utc_time) = 2015 AND
EXTRACT(WEEK FROM utc_time) = 1
or
EXTRACT(YEAR FROM utc_time) = 2015 AND
EXTRACT(MONTH FROM utc_time) = 1
or just
EXTRACT(YEAR FROM utc_time) = 2015
On Thu, Feb 5, 2015 at 10:12 PM, David G Johnston <david.g.johnston@gmail.com> wrote:
Tim Uckun wrote
> 1. Should I be worried about having possibly hundreds of thousands of
> shards.
IIRC, yes.
> 2. Is PG smart enough to handle overlapping constraints on table and limit
> it's querying to only those tables that have the correct time constraint.
Probably yes, but seems easy enough to verify.
All constraints are checked for each partiton and if any return false the
entire partiton will be excluded; which means multiple partitions can be
included.
Note, this is large reason why #1 poses a problem.
David J.
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Melvin Davidson
I reserve the right to fantasize. Whether or not you
wish to share my fantasy is entirely up to you.
I reserve the right to fantasize. Whether or not you
wish to share my fantasy is entirely up to you.
Partitioning by day would result in less partitions but of course it would create a "hot" table where all the writes go.
Actually I have thought of an alternative and I'd be interested in your opinion of it.I leave the metrics table alone, The current code continues to read and write from the metrics. Every night I create a table based on metricts_YYYYMMDD which inherit from metrics and move data (using the "ONLY" clause in the delete) into the table and then set a constraint for that table for that day. I also adjust the constraint for the metrics table which is basically saying "where timestamp > YYYMMDD".
This way there is no trigger in the parent table to slow down the inserts and I still have partitions that will speed up read queries. I realize that moving large amounts of data is going to be painful but perhaps I can do it in chunks.
This way there is no trigger in the parent table to slow down the inserts and I still have partitions that will speed up read queries. I realize that moving large amounts of data is going to be painful but perhaps I can do it in chunks.
On Sat, Feb 7, 2015 at 3:09 AM, Melvin Davidson <melvin6925@gmail.com> wrote:
Perhaps, I do not fully understand completely, but would it not be simpler to just rearrange the key (and partition) by date & location?EG: 2015_01_01_metrics_location_XXXXXIn that way, you would only have 365 partitions per year at most. But you also have the option to break it down by week or month, or year.EG:EXTRACT(YEAR FROM utc_time) = 2015 AND
EXTRACT(WEEK FROM utc_time) = 1or
EXTRACT(YEAR FROM utc_time) = 2015 AND
EXTRACT(MONTH FROM utc_time) = 1or just
EXTRACT(YEAR FROM utc_time) = 2015--On Thu, Feb 5, 2015 at 10:12 PM, David G Johnston <david.g.johnston@gmail.com> wrote:Tim Uckun wrote
> 1. Should I be worried about having possibly hundreds of thousands of
> shards.
IIRC, yes.
> 2. Is PG smart enough to handle overlapping constraints on table and limit
> it's querying to only those tables that have the correct time constraint.
Probably yes, but seems easy enough to verify.
All constraints are checked for each partiton and if any return false the
entire partiton will be excluded; which means multiple partitions can be
included.
Note, this is large reason why #1 poses a problem.
David J.
--
View this message in context: http://postgresql.nabble.com/Partioning-with-overlapping-and-non-overlapping-constraints-tp5836869p5836871.html
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http://www.postgresql.org/mailpref/pgsql-generalMelvin Davidson
I reserve the right to fantasize. Whether or not you
wish to share my fantasy is entirely up to you.
Well, without knowing too much about your application, it certainly sounds like using the metricts_YYYYMMDD is the way to go. As for modifying the constraint daily, couldn't you just use
where timestamp > current_date - Interval '1 Day'
?
where timestamp > current_date - Interval '1 Day'
?
On Mon, Feb 9, 2015 at 5:14 AM, Marc Mamin <M.Mamin@intershop.de> wrote:
>I have two partitioning questions I am hoping somebody can help me with.
>
>I have a fairly busy metric(ish) table. It gets a few million records per day, the data is transactional for a while but then settles down and is used for analytical purposes later.
>
>When a metric is reported both the UTC time and the local times are stored along with the other data belonging to the metric.
Don't you have duplicate information within your UTC, location and local_time data ?
Maybe you can just attach a timezone to each location...
>I want to partition this table to both make it faster to query and also to spread out the writes. Ideally the partitions would be based on the UTC timestamp and the sending location. For example
>
>metrics_location_XXXXX_2015_01_01
>
>First problem with this approach is that there could be tens of thousands of locations so this is going to result hundreds of thousands of tables. I know there are no upper limits to how many tables there are but I am thinking this might really get me into trouble later.
With only a few millions rows per day, weekly or even monthly partitions without regard of locations should be sufficient for older data.
It should be possible to partition your hot data differently; But Instead of using one partition per location, you may use a hash/modulo approach to keep the number of partitions in a reasonable count if required at all (This can be helpful: https://github.com/markokr/pghashlib). Here I would avoid to include time information except for the limit between old and hot tables. And depending on the pattern and performance requirement of your analytic queries this may be sufficient (i.e. don't partition on the time at all).
With smaller partitions for hot data, it should be quite fast to move them one by one to the old data. I have no experience with the trigger based partitioning of Postgres (we handle partitions logically at the application level), so I'm not sure how difficult this approach is. I suppose that you'll need a function that move data from hot to old partitons and that fix the triggers accordingly.
>
>Second and possibly more vexing problem is that often the local time is queried. Ideally I would like to put three constraints on the child tables. Location id, UTC timestamp and the local time but obviously the local timestamps would overlap with other locations in the same timezone Even if I was to only partition by UTC the local timestamps would overlap between tables.
>
>So the questions in a nutshell are.
>
>1. Should I be worried about having possibly hundreds of thousands of shards.
>2. Is PG smart enough to handle overlapping constraints on table and limit it's querying to only those tables that have the correct time constraint.
If you partition on the UTC time only, you don't have overlapping. When querying on the local time, the planner will consider all partitions, but an additional index or constraint on this column should be sufficient as long as your partition count remains small.
regards,
Marc Mamin
--
Melvin Davidson
I reserve the right to fantasize. Whether or not you
wish to share my fantasy is entirely up to you.
I reserve the right to fantasize. Whether or not you
wish to share my fantasy is entirely up to you.
This approach wouldn't work I think. The data keeps growing in the "hot" table.
On Tue, Feb 10, 2015 at 6:01 AM, Melvin Davidson <melvin6925@gmail.com> wrote:
Well, without knowing too much about your application, it certainly sounds like using the metricts_YYYYMMDD is the way to go. As for modifying the constraint daily, couldn't you just use
where timestamp > current_date - Interval '1 Day'
?--On Mon, Feb 9, 2015 at 5:14 AM, Marc Mamin <M.Mamin@intershop.de> wrote:
>I have two partitioning questions I am hoping somebody can help me with.
>
>I have a fairly busy metric(ish) table. It gets a few million records per day, the data is transactional for a while but then settles down and is used for analytical purposes later.
>
>When a metric is reported both the UTC time and the local times are stored along with the other data belonging to the metric.
Don't you have duplicate information within your UTC, location and local_time data ?
Maybe you can just attach a timezone to each location...
>I want to partition this table to both make it faster to query and also to spread out the writes. Ideally the partitions would be based on the UTC timestamp and the sending location. For example
>
>metrics_location_XXXXX_2015_01_01
>
>First problem with this approach is that there could be tens of thousands of locations so this is going to result hundreds of thousands of tables. I know there are no upper limits to how many tables there are but I am thinking this might really get me into trouble later.
With only a few millions rows per day, weekly or even monthly partitions without regard of locations should be sufficient for older data.
It should be possible to partition your hot data differently; But Instead of using one partition per location, you may use a hash/modulo approach to keep the number of partitions in a reasonable count if required at all (This can be helpful: https://github.com/markokr/pghashlib). Here I would avoid to include time information except for the limit between old and hot tables. And depending on the pattern and performance requirement of your analytic queries this may be sufficient (i.e. don't partition on the time at all).
With smaller partitions for hot data, it should be quite fast to move them one by one to the old data. I have no experience with the trigger based partitioning of Postgres (we handle partitions logically at the application level), so I'm not sure how difficult this approach is. I suppose that you'll need a function that move data from hot to old partitons and that fix the triggers accordingly.
>
>Second and possibly more vexing problem is that often the local time is queried. Ideally I would like to put three constraints on the child tables. Location id, UTC timestamp and the local time but obviously the local timestamps would overlap with other locations in the same timezone Even if I was to only partition by UTC the local timestamps would overlap between tables.
>
>So the questions in a nutshell are.
>
>1. Should I be worried about having possibly hundreds of thousands of shards.
>2. Is PG smart enough to handle overlapping constraints on table and limit it's querying to only those tables that have the correct time constraint.
If you partition on the UTC time only, you don't have overlapping. When querying on the local time, the planner will consider all partitions, but an additional index or constraint on this column should be sufficient as long as your partition count remains small.
regards,
Marc MaminMelvin Davidson
I reserve the right to fantasize. Whether or not you
wish to share my fantasy is entirely up to you.