Data Modeling, Normalization, and Denormalisation | FOSDEM '19 | Dimitri Fontaine

As a developer using PostgreSQL one of the most important tasks you have to deal with is modeling the database schema for your application. In order to achieve a solid design, it’s important to understand how the schema is then going to be used as well as the trade-offs it involves.

As Fred Brooks said: “Show me your flowcharts and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won’t usually need your flowcharts; they’ll be obvious.”

In this talk we're going to see practical normalisation examples and their benefits, and also review some anti-patterns and their typical PostgreSQL solutions, including Denormalization techniques thanks to advanced Data Types.

As a developer using PostgreSQL one of the most important tasks you have to deal with is modeling the database schema for your application. In order to achieve a solid design, it’s important to understand how the schema is then going to be used as well as the trade-offs it involves.

As Fred Brooks said: “Show me your flowcharts and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won’t usually need your flowcharts; they’ll be obvious.”

In this talk we're going to see practical normalisation examples and their benefits, and also review some anti-patterns and their typical PostgreSQL solutions, including Denormalization techniques thanks to advanced Data Types.

Transcript

1. Data Modeling, Normalization and Denormalisation Dimitri Fontaine Citus Data F O S D E M 2 0 1 9 , B R U X E L L E S | F E B R U A R Y 3 , 2 0 1 9
3. PostgreSQL P O S T G R E S Q L M A J O R C O N T R I B U T O R
4. Citus Data C U R R E N T L Y W O R K I N G A T
5. pgloader.io
6. Data Modeling
7. Rule 5. Data dominates. R O B P I K E , N O T E S O N P R O G R A M M I N G I N C “If you’ve chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.” (Brooks p. 102)
8. Data Modeling Examples • Data Types • Constraints • Primary keys, Foreign Keys, Check, Not Null • Partial unique indexes • Exclusion Constraints
9. Data Modeling create table sandbox.article ( id bigserial primary key, category integer references sandbox.category(id), pubdate timestamptz, title text not null, content text );
10. Partial Unique Index CREATE TABLE toggles ( user_id integer NOT NULL, type text NOT NULL, enabled_at timestamp NOT NULL, disabled_at timestamp, ); CREATE UNIQUE INDEX ON toggles (user_id, type) WHERE disabled_at IS NULL;
11. Constraints are Guarantees create table rates ( currency text, validity daterange, rate numeric, exclude using gist (currency with =, validity with &&) );
12. Avoiding Database Anomalies
13. Update Anomaly
14. Insertion Anomaly
15. Deletion anomaly
16. Database Design and User Workflow A N O T H E R Q U O T E F R O M F R E D B R O O K S “Show me your flowcharts and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won’t usually need your flowcharts; they’ll be obvious.”
17. Tooling for Database Modeling BEGIN; create schema if not exists sandbox; create table sandbox.category ( id serial primary key, name text not null ); insert into sandbox.category(name) values ('sport'),('news'),('box office'),('music'); ROLLBACK;
18. Object Relational Mapping • The R in ORM stands for relation • Every SQL query result set is a relation
19. Object Relational Mapping • User Workflow • Consistent view of the whole world at all time When mapping base tables, you end up trying to solve different complex issues at the same time
20. Normalization
21. Basics of the Unix Philosophy: principles Clarity • Clarity is better than cleverness Simplicity • Design for simplicity; add complexity only where you must. Transparency • Design for visibility to make inspection and debugging easier. Robustness • Robustness is the child of transparency and simplicity.
22. DRY
23. 1st Normal Form, Codd, 1970 • There are no duplicated rows in the table. • Each cell is single-valued (no repeating groups or arrays). • Entries in a column (field) are of the same kind.
24. 2nd Normal Form, Codd, 1971 “A table is in 2NF if it is in 1NF and if all non- key attributes are dependent on all of the key. A partial dependency occurs when a non-key attribute is dependent on only a part of the composite key.” “A table is in 2NF if it is in 1NF and if it has no partial dependencies.”
25. Third Normal Form, Codd, 1971 BCNF, Boyce-Codd, 1974 • A table is in 3NF if it is in 2NF and if it has no transitive dependencies. • A table is in BCNF if it is in 3NF and if every determinant is a candidate key.
26. More Normal Forms • Each level builds on the previous one. • A table is in 4NF if it is in BCNF and if it has no multi- valued dependencies. • A table is in 5NF, also called “Projection-join Normal Form” (PJNF), if it is in 4NF and if every join dependency in the table is a consequence of the candidate keys of the table. • A table is in DKNF if every constraint on the table is a logical consequence of the definition of keys and domains.
27. Database Constraints
28. Primary Keys create table sandbox.article ( id bigserial primary key, category integer references sandbox.category(id), pubdate timestamptz, title text not null, content text );
29. Surrogate Keys Artificially generated key is named a surrogate key because it is a substitute for natural key. A natural key would allow preventing duplicate entries in our data set.
30. Surrogate Keys insert into sandbox.article (category, pubdate, title) values (2, now(), 'Hot from the Press'), (2, now(), 'Hot from the Press') returning *;
31. Oops. Not a Primary Key. -[ RECORD 1 ]--------------------------- id | 3 category | 2 pubdate | 2018-03-12 15:15:02.384105+01 title | Hot from the Press content | -[ RECORD 2 ]--------------------------- id | 4 category | 2 pubdate | 2018-03-12 15:15:02.384105+01 title | Hot from the Press content | INSERT 0 2
32. Natural Primary Key create table sandboxpk.article ( category integer references sandbox.category(id), pubdate timestamptz, title text not null, content text, primary key(category, pubdate, title) );
33. Update Foreign Keys create table sandboxpk.comment ( a_category integer not null, a_pubdate timestamptz not null, a_title text not null, pubdate timestamptz, content text, primary key(a_category, a_pubdate, a_title, pubdate, content), foreign key(a_category, a_pubdate, a_title) references sandboxpk.article(category, pubdate, title) );
34. Natural and Surrogate Keys create table sandbox.article ( id integer generated always as identity, category integer not null references sandbox.category(id), pubdate timestamptz not null, title text not null, content text, primary key(category, pubdate, title), unique(id) );
35. Other Constraints
36. Normalisation Helpers • Primary Keys • Foreign Keys • Not Null • Check Constraints • Domains • Exclusion Constraints create table rates ( currency text, validity daterange, rate numeric, exclude using gist ( currency with =, validity with && ) );
37. Denormalization
38. Rules of Optimisation
39. Premature Optimization… D O N A L D K N U T H “Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%.” "Structured Programming with Goto Statements” Computing Surveys 6:4 (December 1974), pp. 261–301, §1.
40. Denormalization: cache • Duplicate data for faster access • Implement cache invalidation
41. Denormalization example \set season 2017 select drivers.surname as driver, constructors.name as constructor, sum(points) as points from results join races using(raceid) join drivers using(driverid) join constructors using(constructorid) where races.year = :season group by grouping sets(drivers.surname, constructors.name) having sum(points) > 150 order by drivers.surname is not null, points desc;
42. Denormalization example create view v.season_points as select year as season, driver, constructor, points from seasons left join lateral ( select drivers.surname as driver, constructors.name as constructor, sum(points) as points from results join races using(raceid) join drivers using(driverid) join constructors using(constructorid) where races.year = seasons.year group by grouping sets(drivers.surname, constructors.name) order by drivers.surname is not null, points desc ) as points on true order by year, driver is null, points desc;
43. Materialized View create materialized view cache.season_points as select * from v.season_points; create index on cache.season_points(season);
44. Materialized View refresh materialized view cache.season_points;
45. Application Integration select driver, constructor, points from cache.season_points where season = 2017 and points > 150;
46. Denormalization: audit trails • Foreign key references to other tables won't be possible when those reference changes and you want to keep a history that, by definition, doesn't change. • The schema of your main table evolves and the history table shouldn’t rewrite the history for rows already written.
47. History tables with JSONB create schema if not exists archive; create type archive.action_t as enum('insert', 'update', 'delete'); create table archive.older_versions ( table_name text, date timestamptz default now(), action archive.action_t, data jsonb );
48. Validity Periods create table rates ( currency text, validity daterange, rate numeric, exclude using gist (currency with =, validity with &&) );
49. Validity Periods select currency, validity, rate from rates where currency = 'Euro' and validity @> date '2017-05-18'; -[ RECORD 1 ]--------------------- currency | Euro validity | [2017-05-18,2017-05-19) rate | 1.240740
50. Denormalization Helpers: Data Types
51. Composite Data Types • Composite Type • Arrays • JSONB • Enum • hstore • ltree • intarray • hll
52. Partitioning
53. Partitioning Improvements PostgreSQL 10 • Indexing • Primary Keys • On conflict • Update Keys PostgreSQL 11 • Indexing, Primary Keys, Foreign Keys • Hash partitioning • Default partition • On conflict support • Update Keys
55. Schemaless with JSONB select jsonb_pretty(data) from magic.cards where data @> '{"type":"Enchantment", "artist":"Jim Murray", “colors":["Blue"] }';
56. Durability Trade-Offs create role dbowner with login; create role app with login; create role critical with login in role app inherit; create role notsomuch with login in role app inherit; create role dontcare with login in role app inherit; alter user critical set synchronous_commit to remote_apply; alter user notsomuch set synchronous_commit to local; alter user dontcare set synchronous_commit to off;
57. Per Transaction Durability SET demo.threshold TO 1000; CREATE OR REPLACE FUNCTION public.syncrep_important_delta() RETURNS TRIGGER LANGUAGE PLpgSQL AS $$ DECLARE threshold integer := current_setting('demo.threshold')::int; delta integer := NEW.abalance - OLD.abalance; BEGIN IF delta > threshold THEN SET LOCAL synchronous_commit TO on; END IF; RETURN NEW; END; $$;
58. Horizontal Scaling Sharding with Citus
59. Five Sharding Data Models and which is right? • Sharding by Geography • Sharding by EntityId • Sharding a graph • Time Partitioning
60. Mastering PostgreSQL in Application Development
61. Mastering PostgreSQL In Application Development https://masteringpostgresql.com
62. Mastering PostgreSQL In Application Development -15% “fosdem2019” https://masteringpostgresql.com
63. Ask Me Two Questions! Dimitri Fontaine Citus Data F O S D E M 2 0 1 9 , B R U X E L L E S | F E B R U A R Y 3 , 2 0 1 9