4. synonyms etc.
● Business Intelligence
● also BI/DW
● Analytics database
● OnLine Analytical Processing
(OLAP)
● Data Mining
● Decision Support
5. OLTP vs DW
● many single-row ● few large batch
writes imports
● current data ● years of data
● queries generated ● queries generated
by user activity by large reports
● < 1s response ● queries can run for
times hours
● 0.5 to 5x RAM ● 5x to 2000x RAM
6. OLTP vs DW
● 100 to 1000 users ● 1 to 10 users
● constraints ● no constraints
8. Complex Queries
SELECT
CASE WHEN ((SUM(inventory.closed_on_hand) + SUM(changes.received) + SUM(changes.adjustments) +
SUM(changes.transferred_in-changes.transferred_out)) <> 0) THEN ROUND((CAST(SUM(changes.sold_and_closed +
changes.returned_and_closed) AS numeric) * 100) / CAST(SUM(starting.closed_on_hand) + SUM(changes.received) +
SUM(changes.adjustments) + SUM(changes.transferred_in-changes.transferred_out) AS numeric), 5) ELSE 0 END AS "Percent_Sold",
CASE WHEN (SUM(changes.sold_and_closed) <> 0) THEN ROUND(100*((SUM(changes.closed_markdown_units_sold)*1.0) /
SUM(changes.sold_and_closed)), 5) ELSE 0 END AS "Percent_of_Units_Sold_with_Markdown",
CASE WHEN (SUM(changes.sold_and_closed * _sku.retail_price) <> 0) THEN
ROUND(100*(SUM(changes.closed_markdown_dollars_sold)*1.0) / SUM(changes.sold_and_closed * _sku.retail_price), 5) ELSE 0 END AS
"Markdown_Percent",
'0' AS "Percent_of_Total_Sales",
CASE WHEN SUM((changes.sold_and_closed + changes.returned_and_closed) * _sku.retail_price) IS NULL THEN 0 ELSE
SUM((changes.sold_and_closed + changes.returned_and_closed) * _sku.retail_price) END AS "Net_Sales_at_Retail",
'0' AS "Percent_of_Ending_Inventory_at_Retail", SUM(inventory.closed_on_hand * _sku.retail_price) AS
"Ending_Inventory_at_Retail",
"_store"."label" AS "Store",
"_department"."label" AS "Department",
"_vendor"."name" AS "Vendor_Name"
FROM
inventory
JOIN inventory as starting
ON inventory.warehouse_id = starting.warehouse_id
AND inventory.sku_id = starting.sku_id
LEFT OUTER JOIN
( SELECT warehouse_id, sku_id,
sum(received) as received,
sum(transferred_in) as transferred_in,
sum(transferred_out) as transferred_out,
sum(adjustments) as adjustments,
sum(sold) as sold
FROM movement
WHERE movement.movement_date BETWEEN '2010-08-05' AND '2010-08-19'
GROUP BY sku_id, warehouse_id ) as changes
ON inventory.warehouse_id = changes.warehouse_id
AND inventory.sku_id = changes.sku_id
JOIN _sku ON _sku.id = inventory.sku_id
JOIN _warehouse ON _warehouse.id = inventory.warehouse_id
JOIN _location_hierarchy AS _store ON _store.id = _warehouse.store_id
AND _store.type = 'Store'
JOIN _product ON _product.id = _sku.product_id
JOIN _merchandise_hierarchy AS _department
9. Complex Queries
● JOIN optimization
● 5 different JOIN types
● approximate planning for 20+ table joins
● subqueries in any clause
● plus nested subqueries
● windowing queries
● recursive queries
10. Big Data Features
● big tables partitioning
● big databases tablespaces
● big backups PITR
● big updates binary replication
● big queries resource control
11. Extensibility
● add data analysis functionality from
external libraries inside the database
● financial analysis
● genetic sequencing
● approximate queries
● create your own:
● data types functions
● aggregates operators
12. Community
“I'm running a partitioning scheme using 256 tables with a maximum
of 16 million rows (namely IPv4-addresses) and a current total of
about 2.5 billion rows, there are no deletes though, but lots of
updates.”
“I use PostgreSQL basically as a data warehouse to store all the
genetic data that our lab generates … With this configuration I figure
I'll have ~3TB for my main data tables and 1TB for indexes. ”
● lots of experience with large databases
● blogs, tools, online help
16. How do I configure
PostgreSQL for
data warehousing?
17. General Setup
● Latest version of PostgreSQL
● System with lots of drives
● 6 to 48 drives
– or 2 to 12 SSDs
● High-throughput RAID
● Write ahead log (WAL) on separate disk(s)
● 10 to 50 GB space
19. Settings
few connections
max_connections = 10 to 40
raise those memory limits!
shared_buffers = 1/8 to ¼ of RAM
work_mem = 128MB to 1GB
maintenance_work_mem = 512MB to 1GB
temp_buffers = 128MB to 1GB
effective_cache_size = ¾ of RAM
wal_buffers = 16MB
20. No autovacuum
autovacuum = off
vacuum_cost_delay = off
● do your VACUUMs and ANALYZEs as part
of the batch load process
● usually several of them
● also maintain tables by partitioning
22. logical data extents
● lets you put some of your data on specific
devices / disks
CREATE TABLESPACE 'history_log'
LOCATION '/mnt/san2/history_log';
ALTER TABLE history_log TABLESPACE
history_log;
23. tablespace reasons
● parallelize access
● your largest “fact table” on one tablespace
● its indexes on another
– not as useful if you have a good SAN
● temp tablespace for temp tables
● move key join tables to SSD
● migrate to new storage one table at a time
25. Extract, Transform, Load
● how you turn external raw data into
normalized database data
● Apache logs → web analytics DB
● CSV POS files → financial reporting DB
● OLTP server → 10-year data warehouse
● also called ELT when the transformation is
done inside the database
● PostgreSQL is particularly good for ELT
26. L: INSERT
● batch INSERTs into 100's or 1000's per
transaction
● row-at-a-time is very slow
● create and load import tables in one
transaction
● add indexes and constraints after load
● insert several streams in parallel
● but not more than CPU cores
27. L: COPY
● Powerful, efficient delimited file loader
● almost bug-free - we use it for backup
● 3-5X faster than inserts
● works with most delimited files
● Not fault-tolerant
● also have to know structure in advance
● try pg_loader for better COPY
28. L: COPY
COPY weblog_new FROM
'/mnt/transfers/weblogs/weblog-
20110605.csv' with csv;
COPY traffic_snapshot FROM
'traffic_20110605192241' delimiter
'|' nulls as 'N';
copy weblog_summary_june TO
'Desktop/weblog-june2011.csv' with
csv header;
29. L: in 9.1: FDW
CREATE FOREIGN TABLE raw_hits
( hit_time TIMESTAMP,
page TEXT )
SERVER file_fdw
OPTIONS (format 'csv', delimiter
';', filename '/var/log/hits.log');
30. L: in 9.1: FDW
CREATE TABLE hits_2011041617 AS
SELECT page, count(*)
FROM raw_hits
WHERE hit_time >
'2011-04-16 16:00:00' AND
hit_time <= '2011-04-16 17:00:00'
GROUP BY page;
31. T: temporary tables
CREATE TEMPORARY TABLE
ON COMMIT DROP
sales_records_june_rollup AS
SELECT seller_id, location,
sell_date, sum(sale_amount),
array_agg(item_id)
FROM raw_sales
WHERE sell_date BETWEEN '2011-06-01'
AND '2011-06-30 23:59:59.999'
GROUP BY seller_id, location,
sell_date;
32. in 9.1: unlogged tables
● like myISAM without the risk
CREATE UNLOGGED TABLE
cleaned_log_import
AS SELECT hit_time, page
FROM raw_hits, hit_watermark
WHERE hit_time > last_watermark
AND is_valid(page);
33. T: stored procedures
● multiple languages
● SQL PL/pgSQL
● PL/Perl PL/Python PL/PHP
● PL/R PL/Java
● allows you to use exernal data processing
libraries in the database
● custom aggregates, operators, more
34. CREATE OR REPLACE FUNCTION normalize_query ( queryin text )
RETURNS TEXT LANGUAGE PLPERL STABLE STRICT AS $f$
# this function "normalizes" queries by stripping out constants.
# some regexes by Guillaume Smet under The PostgreSQL License.
local $_ = $_[0];
#first cleanup the whitespace
s/s+/ /g;
s/s,/,/g;
s/,(S)/, $1/g;
s/^s//g;
s/s$//g;
#remove any double quotes and quoted text
s/'//g;
s/'[^']*'/''/g;
s/''('')+/''/g;
#remove TRUE and FALSE
s/(W)TRUE(W)/$1BOOL$2/gi;
s/(W)FALSE(W)/$1BOOL$2/gi;
#remove any bare numbers or hex numbers
s/([^a-zA-Z_$-])-?([0-9]+)/${1}0/g;
s/([^a-z_$-])0x[0-9a-f]{1,10}/${1}0x/ig;
#normalize any IN statements
s/(INs*)(['0x,s]*)/${1}(...)/ig;
#return the normalized query
return $_;
$f$;
35. CREATE OR REPLACE FUNCTION f_graph2() RETURNS text AS '
sql <- paste("SELECT id as x,hit as y FROM mytemp LIMIT
30",sep="");
str <- c(pg.spi.exec(sql));
mymain <- "Graph 2";
mysub <- paste("The worst offender is: ",str[1,3]," with
",str[1,2]," hits",sep="");
myxlab <- "Top 30 IP Addresses";
myylab <- "Number of Hits";
pdf(''/tmp/graph2.pdf'');
plot(str,type="b",main=mymain,sub=mysub,xlab=myxlab,ylab
=myylab,lwd=3);
mtext("Probes by intrusive IP Addresses",side=3);
dev.off();
print(''DONE'');
' LANGUAGE plr;
36.
37. ELT Tips
● bulk insert into a new table instead of
updating/deleting an existing table
● update all columns in one operation
instead of one at a time
● use views and custom functions to simplify
your queries
● inserting into your long-term tables should
be the very last step – no updates after!
42. SELECT MAX(concurrent)
FROM (
SELECT SUM(tally)
OVER (ORDER BY start)
AS concurrent
FROM (
SELECT start, 1::INT as tally
FROM events
UNION ALL
SELECT (start + duration), -1
FROM events )
AS event_vert) AS ec;
43. UPDATE partition_name SET drop_month = dropit
FROM (
SELECT round_id,
CASE WHEN ( ( row_number() over
(partition by team_id order by team_id, total_points) )
<= ( drop_lowest ) ) THEN 0 ELSE 1 END as dropit
FROM (
SELECT team.team_id, round.round_id, month_points as total_points,
row_number() OVER (
partition by team.team_id, kal.positions
order by team.team_id, kal.positions,
month_points desc ) as ordinal,
at_least, numdrop as drop_lowest
FROM partition_name as rdrop
JOIN round USING (round_id)
JOIN team USING (team_id)
JOIN pick ON round.round_id = pick.round_id
and pick.pick_period @> this_period
LEFT OUTER JOIN keep_at_least kal
ON rdrop.pool_id = kal.pool_id
and pick.position_id = any ( kal.positions )
WHERE rdrop.pool_id = this_pool
AND team.team_id = this_team ) as ranking
WHERE ordinal > at_least or at_least is null
) as droplow
WHERE droplow.round_id = partition_name .round_id
AND partition_name .pool_id = this_pool AND dropit = 0;
44. SELECT round_id,
CASE WHEN ( ( row_number()
OVER
(partition by team_id
order by team_id, total_points) )
<= ( drop_lowest ) )
THEN 0 ELSE 1 END as dropit
FROM (
SELECT team.team_id, round.round_id,
month_points as total_points,
row_number() OVER (
partition by team.team_id,
kal.positions
order by team.team_id,
kal.positions, month_points
desc ) as ordinal
45. stream processing SQL
● replace multiple queries with a single
query
● avoid scanning large tables multiple times
● replace pages of application code
● and MB of data transmission
● SQL alternative to map/reduce
● (for some data mining tasks)
47. Postgres partitioning
● based on table inheritance and constraint
exclusion
● partitions are also full tables
● explicit constraints define the range of the
partion
● triggers or RULEs handle insert/update
48. CREATE TABLE sales (
sell_date TIMESTAMPTZ NOT NULL,
seller_id INT NOT NULL,
item_id INT NOT NULL,
sale_amount NUMERIC NOT NULL,
narrative TEXT );
50. CREATE FUNCTION sales_insert ()
RETURNS trigger
LANGUAGE plpgsql AS $f$
BEGIN
CASE WHEN sell_date < '2011-06-01'
THEN INSERT INTO sales_2011_05 VALUES (NEW.*)
WHEN sell_date < '2011-07-01'
THEN INSERT INTO sales_2011_06 VALUES (NEW.*)
WHEN sell_date >= '2011-07-01'
THEN INSERT INTO sales_2011_07 VALUES (NEW.*)
ELSE
INSERT INTO sales_overflow VALUES (NEW.*)
END;
RETURN NULL;
END;$f$;
CREATE TRIGGER sales_insert BEFORE INSERT ON sales
FOR EACH ROW EXECUTE PROCEDURE sales_insert();
51. Postgres partitioning
● Good for: ● Bad for:
● “rolling off” data ● administration
● DB maintenance ● queries which do
● queries which use not use the
the partition key partition key
● under 300
● JOINs
partitions ● over 300 partitions
● insert performance ● update
performance
52. you need a data
expiration policy
● you can't plan your DW otherwise
● sets your storage requirements
● lets you project how queries will run when
database is “full”
● will take a lot of meetings
● people don't like talking about deleting data
53. you need a data
expiration policy
● raw import data 1 month
● detail-level transactions 3 years
● detail-level web logs 1 year
● rollups 10 years
55. query results as table
● calculate once, read many time
● complex/expensive queries
● frequently referenced
● not necessarily a whole query
● often part of a query
● manually maintained in PostgreSQL
● automagic support not complete yet
56. SELECT page,
COUNT(*) as total_hits
FROM hit_counter
WHERE date_trunc('day', hit_date)
BETWEEN ( now()
AND now() - INTERVAL '7 days' )
ORDER BY total_hits DESC LIMIT 10;
58. each day:
INSERT INTO page_hits
SELECT page,
date_trunc('day', hit_date)
as hit_day,
COUNT(*) as total_hits
FROM hit_counter
WHERE date_trunc('day', hit_date)
= date_trunc('day',
now() - INTERVAL '1 day')
ORDER BY total_hits DESC;
60. maintaining matviews
BEST: update matviews
at batch load time
GOOD: update matview according
to clock/calendar
BAD for DW: update matviews
using a trigger
61. matview tips
● matviews should be small
● 1/10 to ¼ of RAM
● each matview should support several
queries
● or one really really important one
● truncate + insert, don't update
● index matviews like crazy
62. Contact
● Josh Berkus: josh@pgexperts.com
● blog: blogs.ittoolbox.com/database/soup
● PostgreSQL: www.postgresql.org
● pgexperts: www.pgexperts.com
● Upcoming Events
● pgCon: Ottawa: May 17-20
● OpenSourceBridge: Portland: June
This talk is copyright 2010 Josh Berkus and is licensed under the creative commons attribution
license. Special thanks for materials to: Elein Mustain (PL/R), Hitoshi Harada and David Fetter
(windowing functions), Andrew Dunstan (file_FDW)
