The document discusses PostgreSQL, highlighting its features, capabilities, and limitations. It emphasizes PostgreSQL's compliance with ACID principles, support for full-text search, and various data types while outlining practical implementations and comparisons with other search engines. Additionally, the author shares personal insights and experiences in utilizing PostgreSQL for web applications.

1. PostgreSQL
It’s kind’ve a nifty database

2. How do you pronounce it?
Thanks to Thad for the suggestion
Answer Response Percentage
post-gres-q-l 2379 45%
post-gres 1611 30%
pahst-grey 24 0%
pg-sequel 50 0%
post-gree 350 6%
postgres-sequel 574 10%
p-g 49 0%
database 230 4%
Total 5267

3. Who is this guy?
• I’m Barry Jones
• Been a developing web apps since ’98
• Not a DBA
• Performance and infrastructure nut
• Pragmatic Idealist
– Prefer the best solution possible for current circumstances vs the best solution
possible at all costs

4. What is PostgreSQL NOT?
• NOT a silver bullet
• NOT the answer to life, the universe and
everything
• NOT better at everything than everybody
• NOT always the best option for your needs
• NOT used to it’s potential in most cases
• NOT owned by Oracle or Microsoft

5. What IS PostgreSQL?
• Fully ACID compliant
• Feature rich and extensible
• Fast, scalable and leverages multicore
processors very well
• Enterprise class with quality corporate
support options
• Free as in beer
• It’s kind’ve nifty

6. Laundry List of Features
• Multi-version Concurrency Control (MVCC)
• Point in Time Recovery
• Tablespaces
• Asynchronous replication
• Nested Transactions
• Online/hot backups
• Genetic query optimizer multiple index types
• Write ahead logging (WAL)
• Internationalization: character sets, locale-aware sorting, case sensitivity,
formatting
• Full subquery support
• Multiple index scans per query
• ANSI-SQL:2008 standard conformant
• Table inheritance
• LISTEN / NOTIFY event system
• Ability to make a Power Point slide run out of room

7. What are we covering today?
• Full text-search
• Built in data types
• User defined data types
• Automatic data compression
• A look at some other cool features and
extensions, depending how we’re doing on
time

8. Full-text Search
• What about…?
– Solr
– Elastic Search
– Sphinx
– Lucene
– MySQL
• All have their purpose
– Distributed search of multiple document types
• Sphinx
– Client search performance is all that matters
• Solr
– Search constantly incoming data with
streaming index updates
• Elastic Search excels
– You really like Java
• Lucene
– You want terrible search results that don’t even
make sense to you much less your users
• MySQL full text search = the worst thing in the world

9. Full-text Search
• Complications of stand alone search engines
– Data synchronization
• Managing deltas, index updates
• Filtering/deleting/hiding expired data
• Search server outages, redundancy
– Learning curve
– Character sets match up with my database?
– Additional hardware / servers just for search
– Can feel like a black box when you get a support
question asking “why is/isn’t this showing up?”

10. Full-text Search
• But what if your needs are more like:
– Search within my database
– Avoid syncing data with outside systems
– Avoid maintaining outside systems
– Less black box, more control

11. Full-text Search
• tsvector
– The text to be searched
• tsquery
– The search query
• to_tsvector(„the church is AWESOME‟) @@ to_tsquery(SEARCH)
• @@ to_tsquery(„church‟) == true
• @@ to_tsquery(„churches‟) == true
• @@ to_tsquery(„awesome‟) == true
• @@ to_tsquery(„the‟) == false
• @@ to_tsquery(„churches & awesome‟) == true
• @@ to_tsquery(„church & okay‟) == false
• to_tsvector(„the church is awesome‟)
– 'awesom':4 'church':2
• to_tsvector(„simple‟,‟the church is awesome‟)
– 'are':3 'awesome':4 'church':2 'the':1

12. Full-text Search
• ALTER TABLE mytable ADD COLUMN search_vector tsvector
• UPDATE mytable
SET search_vector = to_tsvector(„english‟,coalesce(title,‟‟) || „ „ ||
coalesce(body,‟‟) || „ „ || coalesce(tags,‟‟))
• CREATE INDEX search_text ON mytable USING gin(search_vector)
• SELECT some, columns, we, need
FROM mytable
WHERE search_vector @@ to_tsquery(„english‟,„Jesus & awesome‟)
ORDER BY ts_rank(search_vector,to_tsquery(„english‟,„Jesus & awesome‟))
DESC
• CREATE TRIGGER search_update BEFORE INSERT OR UPDATE
ON mytable FOR EACH ROW EXECUTE PROCEDURE
tsvector_update_trigger(search_vector, ‟english‟, title, body, tags)

13. Full-text Search
• CREATE FUNCTION search_trigger RETURNS trigger AS $$
begin
new.search_vector :=
setweight(to_tsvector(„english‟,coalesce(new.title,‟‟)),‟A‟) ||
setweight(to_tsvector(„english‟,coalesce(new.body,‟‟)),‟D‟) ||
setweight(to_tsvector(„english‟,coalesce(new.tags,‟‟)),‟B‟);
return new;
end
$$ LANGUAGE plpgsql;
• CREATE TRIGGER search_vector_update
BEFORE INSERT OR UPDATE OF title, body, tags ON mytable
FOR EACH ROW EXECUTE PROCEDURE search_trigger();

14. Full-text Search
• A variety of dictionaries
– Various Languages
– Thesaurus
– Snowball, Stem, Ispell, Synonym
– Write your own
• ts_headline
– Snippet extraction and highlighting

15. Datatypes: ranges
• int4range, int8range, numrange, tsrange, tstzrange, daterange
• SELECT int4range(10,20) @> 3 == false
• SELECT numrange(11.1,22.2) && numrange(20.0,30.0) == true
• SELECT int4range(10,20) * int4range(15,25) == 15-20
• CREATE INDEX res_index ON schedule USING gist(during)
• ALTER TABLE schedule ADD EXCLUDE USING gist (during WITH &&)
ERROR: conflicting key value violates exclusion constraint
”schedule_during_excl”
DETAIL: Key (during)=([ 2010-01-01 14:45:00, 2010-01-01
15:45:00 )) conflicts with existing key (during)=([ 2010-01-01
14:30:00, 2010-01-01 15:30:00 )).

16. Datatypes: hstore
• properties
– {“author” => “John Grisham”, “pages” => 535}
– {“director” => “Jon Favreau”, “runtime” = 126}
• SELECT … FROM mytable
WHERE properties -> „director‟ LIKE „%Favreau‟
– Does not use an index
• WHERE properties @> („author‟ LIKE “%Grisham”)
– Uses an index to only check properties with an „author‟
• CREATE INDEX table_properties ON mytable USING gin(properties)

17. Datatypes: arrays
• CREATE TABLE sal_emp(name text, pay_by_quarter integer[],
schedule text[][])
• CREATE TABLE tictactoe ( squares integer[3][3] )
• INSERT INTO tictactoe VALUES („{{1,2,3},{4,5,6},{7,8,9}}‟)
• SELECT squares[1:2][1:1] == {{1},{4}}
• SELECT squares[2:3][2:3] == {{5,6},{8,9}}

18. Datatypes: JSON
• Validate JSON structure
• Convert row to JSON
• Functions and operators very similar to hstore

19. Datatypes: XML
• Validates well-formed XML
• Stores like a TEXT field
• XML operations like Xpath
• Can’t index XML column but you can index the
result of an Xpath function

20. Data compression with TOAST
• TOAST = The Oversized Attribute Storage Technique
• TOASTable data is automatically TOASTed
• Example:
– stored a 2.2m XML document
– storage size was 81k

21. User created datatypes
• Built in types
– Numerics, monetary, binary, time, date, interval, boolean,
enumerated, geometric, network address, bit string, text search, UUID,
XML, JSON, array, composite, range
– Add-ons for more such as UPC, ISBN and more
• Create your own types
– Address (contains 2 streets, city, state, zip, country)
– Define how your datatype is indexed
– GIN and GiST indexes are used by custom datatypes

22. Further exploration: PostGIS
• Adds Geographic datatypes
• Distance, area, union, intersection, perimeter
• Spatial indexes
• Tools to load available geographic data
• Distance, Within, Overlaps, Touches, Equals,
Contains, Crosses
• SELECT name, ST_AsText(geom)
FROM nyc_subway_stations
WHERE name = „Broad St‟
• SELECT name, boroname
FROM nyc_neighborhoods
WHERE ST_Intersects(geom,
ST_GeomFromText(„POINT(583571 4506714)‟,26918)
• SELECT sub.name, nh.name, nh.borough
FROM nyc_neighborhoods AS nh
JOIN nyc_subway_stations AS sub
ON ST_Contains(nh.geom, sub.geom)
WHERE sub.name = „Broad St”

23. Further exploration: Functions
• Can be used in queries
• Can be used in stored procedures and triggers
• Can be used to build indexes
• Can be used as table defaults
• Can be written in PL/pgSQL, PL/Tcl, PL/Perl,
PL/Python out of the box
• PL/V8 is available an an extension to use
Javascript

24. Further exploration: PLV8
• CREATE OR REPLACE FUNCTION plv8_test(keys text[], vals text[])
RETURNS text AS $$
var o = {};
for(var i = 0; i < keys.length; i++) {
o[keys[i]] = vals[i];
}
return JSON.stringify(o);
$$ LANGUAGE plv8 IMMUTABLE STRICT;
SELECT plv8_test(ARRAY[„name‟,‟age‟],ARRAY[„Tom‟,‟29‟]);
• CREATE TYPE rec AS (i integer, t text);
CREATE FUNCTION set_of_records RETURNS SETOF rec AS $$
plv8.return_next({“i”: 1,”t”: ”a”});
plv8.return_next({“i”: 2,”t”: “b”});
$$ LANGUAGE plv8;
SELECT * FROM set_of_records();

25. Further exploration: Async commands
/ indexes
• Fine grained control within functions
– PQsendQuery
– PQsendQueryParams
– PQsendPrepare
– PQsendQueryPrepared
– PQsendDescribePrepared
– PQgetResult
– PQconsumeInput
• Per connection asynchronous commits
– set synchronous_commit = off
• Concurrent index creation to avoid blocking large tables
– CREATE INDEX CONCURRENTLY big_index ON mytable (things)

26. Thanks!

27. References / Credits
• NOTE: Some code samples in this presentation have minor
alterations for presentation clarity (such as leaving out
dictionary specifications on some search calls, etc)
• http://www.postgresql.org/docs/9.2/static/index.html
• http://workshops.opengeo.org/postgis-intro/
• http://stackoverflow.com/questions/15983152/how-can-i-find-out-
how-big-a-large-text-field-is-in-postgres
• https://devcenter.heroku.com/articles/heroku-postgres-
extensions-postgis-full-text-search
• http://railscasts.com/episodes/345-hstore?view=asciicast
• http://www.slideshare.net/billkarwin/full-text-search-in-
postgresql