On 19/08/16 10:57, Thomas Güttler wrote:
What do you think?
I store most of my logs in flat textfiles syslog style, and use grep for adhoc querying.
200K rows/day, thats 1.4 million/week, 6 million/month, pretty soon you're talking big tables.
in fact thats several rows/second on a 24/7 basis
There is no need to store them more then 6 weeks in my current use case.
Hi,
to me this kind of data looks like something Postgres can handle with ease.
We're talking about 8.4M rows here.
Coincidentally, I was trying out the new parallel query feature in the
9.6 beta just now and decided to use your numbers as a test case :)
I can create 8.4M records having a timestamp and a random ~ 250 character string
in ~ 31 seconds:
pg96=# select now() + (sec / 200000.0 * 86400.0 || ' seconds')::interval as ts,
pg96-# repeat(random()::text, 15) as msg
pg96-# into t1
pg96-# from generate_series(1, 6 * 7 * 200000) as sec;
SELECT 8400000
Time: 30858.274 ms
Table size is 2.4 GB.
This gives about 6 weeks. A query to scan the whole thing on the narrow column
takes ~ 400 msec, like this:
pg96=# select min(ts), max(ts) from t1;
min | max
-------------------------------+-------------------------------
2016-08-19 20:17:24.921333+00 | 2016-09-30 20:17:24.489333+00
(1 row)
Time: 409.468 ms
Even running an unanchored regular expression (!) on the wider column is doable:
pg96=# select count(*) from t1 where msg ~ '12345';
count
-------
955
(1 row)
Time: 3146.838 ms
If you have some filter, not everything needs to be regexped and this gets pretty fast:
pg96=# select count(*) from t1 where ts between '2016-08-25' and '2016-08-26' and msg ~ '12345';
count
-------
24
(1 row)
Time: 391.577 ms
All this is without indices. Your data is more structured than my test, so undoubtly you will
get some gain from indices...
Here is something more analytical - basically same as the count(*) above:
pg96=# select ts::date, count(*) from t1 where msg ~ '12345' group by ts::date order by ts::date;
ts | count
------------+-------
2016-08-19 | 26
2016-08-20 | 28
[...]
2016-09-28 | 21
2016-09-29 | 33
(42 rows)
Time: 3157.010 ms
Note, however, that I'm using 9.6 beta with the parallel query feature: the sequential scans with the regexp is
run in parallel on 6 workers... this gives me a speed-up of a factor 4-5 (machine has 8 logical CPUs) and
the whole table fits in cache. For a use case as this, the parallel query feature in 9.6 is so good it's almost
like cheating ;)
Bye,
Chris.
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