I need to develop a key/value backend, something like this:
Table T1 id-PK, Key - string, Value - string
INSERT into T1('String1', 'Value1')
INSERT INTO T1('String1', 'Value2')
Table T2 id-PK2, id2->external key to id
some other data in T2, which references data in T1 (like users which have those K/V etc)
I heard about PostgreSQL hstore with GIN/GIST. What is better (performance-wise)?
Doing this the traditional way with SQL joins and having separate columns(Key/Value) ?
Does PostgreSQL hstore perform better in this case?
The format of the data should be any key=>any value.
I also want to do text matching e.g. partially search for (LIKE % in SQL or using the hstore equivalent).
I plan to have around 1M-2M entries in it and probably scale at some point.
What do you recommend ? Going the SQL traditional way/PostgreSQL hstore or any other distributed key/value store with persistence?
If it helps, my server is a VPS with 1-2GB RAM, so not a pretty good hardware. I was also thinking to have a cache layer on top of this, but I think it rather complicates the problem. I just want good performance for 2M entries. Updates will be done often but searches even more often.
Thanks.
Your question is unclear because your not clear about your objective.
The key here is the index (pun intended) - if your dealing with a large amount of keys you want to be able to retrieve them with a the least lookups and without pulling up unrelated data.
Short answer is you probably don't want to use hstore, but lets look into more detail...
Does each id have many key/value pairs (hundreds+)? Don't use hstore.
Will any of your values contain large blocks of text (4kb+)? Don't use hstore.
Do you want to be able to search by keys in wildcard expressions? Don't use hstore.
Do you want to do complex joins/aggregation/reports? Don't use hstore.
Will you update the value for a single key? Don't use hstore.
Multiple keys with the same name under an id? Can't use hstore.
So what's the use of hstore? Well, one good scenario would be if you wanted to hold key/value pairs for an external application where you know you always want to retrive all key/values and will always save the data back as a block (ie, it's never edited in-place). At the same time you do want some flexibility to be able to search this data - albiet very simply - rather than storing it in say a block of XML or JSON. In this case since the number of key/value pairs are small you save on space because your compressing several tuples into one hstore.
Consider this as your table:
CREATE TABLE kv (
id /* SOME TYPE */ PRIMARY KEY,
key_name TEXT NOT NULL,
key_value TEXT,
UNIQUE(id, key_name)
);
I think the design is poorly normalized. Try something more like this:
CREATE TABLE t1
(
t1_id serial PRIMARY KEY,
<other data which depends on t1_id and nothing else>,
-- possibly an hstore, but maybe better as a separate table
t1_props hstore
);
-- if properties are done as a separate table:
CREATE TABLE t1_properties
(
t1_id int NOT NULL REFERENCES t1,
key_name text NOT NULL,
key_value text,
PRIMARY KEY (t1_id, key_name)
);
If the properties are small and you don't need to use them heavily in joins or with fancy selection criteria, and hstore may suffice. Elliot laid out some sensible things to consider in that regard.
Your reference to users suggests that this is incomplete, but you didn't really give enough information to suggest where those belong. You might get by with an array in t1, or you might be better off with a separate table.
Related
Could somebody tell is it good idea use varchar as PK. I mean is it less efficient or equal to int/uuid?
In example: car VIN I want to use it as PK but I'm not sure as good it will be indexed or work as FK or maybe there is some pitfalls.
It depends on which kind of data you are going to store.
In some cases (I would say in most cases) it is better to use integer-based primary keys:
for instance, bigint needs only 8 bytes, varchar can require more space. For this reason, a varchar comparison is often more costly than a bigint comparison.
while joining tables it would be more efficient to join them using integer-based values rather that strings
an integer-based key as a unique key is more appropriate for table relations. For instance, if you are going to store this primary key in another tables as a separate column. Again, varchar will require more space in other table too (see p.1).
This post on stackexchange compares non-integer types of primary keys on a particular example.
The title isn't very specific, so I'll elaborate.
I'm working on a database system in which users can add data to a postgres database though a watered-down API.
So far, all the user's data is compiled into one table, structured similar this:
CREATE TABLE UserData (
userId int NOT NULL,
dataId int NOT NULL PRIMARY KEY,
key varchar(255) NOT NULL,
data json not NOT NULL,
);
However, I am thinking that it may be more efficient (and a faster query) to instead give each userId it's own table:
CREATE TABLE UserData_{userId} (
dataId int NOT NULL PRIMARY KEY,
key varchar(255) NOT NULL,
data json not NOT NULL,
);
CREATE TABLE UserData_{anotherUserId} ();
etc...
I am worried that this will clog up the database, however.
What are the pros and cons for each? Under what load/speed requirements would each serve well? And which of these do you think would be better for a high-load, high-speed scenario?
What you are suggesting is essentially partitioning, so I suggest reading the docs about that. It's mainly advantageous when your operations each cover most of one partition (i.e. select all data for one user, or delete all data for one user).
Most use cases, however, are better served by having one properly indexed table. It's a much simpler structure, and can be very performant. If all of your queries are for a single user, then you'll want all of the indexes to start with the userId column, and postgres will use them to efficiently reach only the relevant rows. And if a day comes when you want to query data across multiple users, it will be much easier to do that.
I advise you not to take my word for it, though. Create both structures, generate fake data to fill them up, and see how they behave!
Consider:
You might end up with x amount of tables if you have one per user. How many "users" do you expect?
The json data is unbound and might grow as your solution/app grows. How will you handle missing keys/values?
The Users table will grow horizontally (more columns) where you should always aim to grow vertically (more rows)
A better solution would be to hold your data in tables related to the user_id.
ie. a "keys" table which holds the key, date_added, active and foreign key (user_id)
This will also solve saving your data as a json which, in you example, will be difficult to maintain. Rather open that json up into a table where you can benefit from indexes and clustering.
If you reference your user_id in separate tables as a foreign key, you can partition or cluster these tables on that key to significantly increase speed and compensate for growth. Which means you have a single table for users (id, name, active, created_at, ...) and lots of tables linked to that user, eg.
subscriptions (id, user_id, ...), items (id, user_id, ...), things (id,user_id, ...)
The past two days I've been reading a lot about jsonb, full text search, gin index, trigram index and what not but I still can not find a definitive or at least a good enough answer on how to fastly search if a row of type JSONB contains certain string as a value. Since it's a search functionality the behavior should be like that of ILIKE
What I have is:
Table, lets call it app.table_1 which contains a lot of columns one of which is of type JSONB, so lets call it column_jsonb
The data inside column_jsonb will always be flatten (no nested objects, etc) but the keys can vary. An example of the data in the column with obfuscated values looks like this:
"{""Key1"": ""Value1"", ""Key2"": ""Value2"", ""Key3"": null, ""Key4"": ""Value4"", ""Key5"": ""Value5""}"
I have a GIN index for this column which doesn't seems to affect the search time significantly (I am testing with 20k records now which takes about 550ms). The indes looks like this:
CREATE INDEX ix_table_1_column_jsonb_gin
ON app.table_1 USING gin
(column_jsonb jsonb_path_ops)
TABLESPACE pg_default;
I am interested only in the VALUES and the way I am searching them now is this:
EXISTS(SELECT value FROM jsonb_each(column_jsonb) WHERE value::text ILIKE search_term)
Here search_term is variable coming from the front end with the string that the user is searching for
I have the following questions:
Is it possible to make the check faster without modifying the data model? I've read that trigram index might be usfeul for similar cases but at least for me it seems that converting jsonb to text and then checking will be slower and actually I am not sure if the trigram index will actually work if the column original type is JSONB and I explicitly cast each row to text? If I'm wroing I would really appreciate some explanation with example if possible.
Is there some JSONB function that I am not aware of which offers what I am searching for out of the box, I'm constrained to PostgreSQL v 11.9 so some new things coming with version 12 are not available for me.
If it's not possible to achieve significant improvement with the current data structure can you propose a way to restructure the data in column_jsonb maybe another column of some other type with data persisted in some other way, I don't know...
Thank you very much in advance!
If the data structure is flat, and you regularly need to search the values, and the values are all the same type, a traditional key/value table would seem more appropriate.
create table table1_options (
table1_id bigint not null references table1(id),
key text not null,
value text not null
);
create index table1_options_key on table1_options(key);
create index table1_options_value on table1_options(value);
select *
from table1_options
where value ilike 'some search%';
I've used simple B-Tree indexes, but you can use whatever you need to speed up your particular searches.
The downsides are that all values must have the same type (doesn't seem to be a problem here) and you need an extra table for each table. That last one can be mitigated somewhat with table inheritance.
Imagine a table with the following structure on PostgreSQL 9.0:
create table raw_fact_table (text varchar(1000));
For the sake of simplification I only mention one text column, in reality it has a dozen. This table has 10 billion rows and each column has lots of duplicates. The table is created from a flat file (csv) using COPY FROM.
To increase performance I want to convert to the following star schema structure:
create table dimension_table (id int, text varchar(1000));
The fact table would then be replaced with a fact table like the following:
create table fact_table (dimension_table_id int);
My current method is to essentially run the following query to create the dimension table:
Create table dimension_table (id int, text varchar(1000), primary key(id));
then to create fill the dimension table I use:
insert into dimension_table (select null, text from raw_fact_table group by text);
Afterwards I need to run the following query:
select id into fact_table from dimension inner join raw_fact_table on (dimension.text = raw_fact_table.text);
Just imagine the horrible performance I get by comparing all strings to all other strings several times.
On MySQL I could run a stored procedure during the COPY FROM. This could create a hash of a string and all subsequent string comparison is done on the hash instead of the long raw string. This does not seem to be possible on PostgreSQL, what do I do then?
Sample data would be a CSV file containing something like this (I use quotes also around integers and doubles):
"lots and lots of text";"3";"1";"2.4";"lots of text";"blabla"
"sometext";"30";"10";"1.0";"lots of text";"blabla"
"somemoretext";"30";"10";"1.0";"lots of text";"fooooooo"
Just imagine the horrible performance
I get by comparing all strings to all
other strings several times.
When you've been doing this a while, you stop imagining performance, and you start measuring it. "Premature optimization is the root of all evil."
What does "billion" mean to you? To me, in the USA, it means 1,000,000,000 (or 1e9). If that's also true for you, you're probably looking at between 1 and 7 terabytes of data.
My current method is to essentially
run the following query to create the
dimension table:
Create table dimension_table (id int, text varchar(1000), primary key(id));
How are you gonna fit 10 billion rows into a table that uses an integer for a primary key? Let's even say that half the rows are duplicates. How does that arithmetic work when you do it?
Don't imagine. Read first. Then test.
Read Data Warehousing with PostgreSQL. I suspect these presentation slides will give you some ideas.
Also read Populating a Database, and consider which suggestions to implement.
Test with a million (1e6) rows, following a "divide and conquer" process. That is, don't try to load a million at a time; write a procedure that breaks it up into smaller chunks. Run
EXPLAIN <sql statement>
You've said you estimate at least 99% duplicate rows. Broadly speaking, there are two ways to get rid of the dupes
Inside a database, not necessarily the same platform you use for production.
Outside a database, in the filesystem, not necessarily the same filesystem you use for production.
If you still have the text files that you loaded, I'd consider first trying outside the database. This awk one-liner will output unique lines from each file. It's relatively economical, in that it makes only one pass over the data.
awk '!arr[$0]++' file_with_dupes > file_without_dupes
If you really have 99% dupes, by the end of this process you should have reduced your 1 to 7 terabytes down to about 50 gigs. And, having done that, you can also number each unique line and create a tab-delimited file before copying it into the data warehouse. That's another one-liner:
awk '{printf("%d\t%s\n", NR, $0);}' file_without_dupes > tab_delimited_file
If you have to do this under Windows, I'd use Cygwin.
If you have to do this in a database, I'd try to avoid using your production database or your production server. But maybe I'm being too cautious. Moving several terabytes around is an expensive thing to do.
But I'd test
SELECT DISTINCT ...
before using GROUP BY. I might be able to do some tests on a large data set for you, but probably not this week. (I don't usually work with terabyte-sized files. It's kind of interesting. If you can wait.)
Just to questions:
- it neccessary to convert your data in 1 or 2 steps?
- May we modify the table while converting?
Running more simplier queries may improve your performance (and the server load while doing it)
One approach would be:
generate dimension_table (If i understand it correctly, you don't have performance problems with this) (maybe with an additional temporary boolean field...)
repeat: choose one previously not selected entry from dimension_table, select every rows from raw_fact_table containing it and insert them into fact_table. Mark dimension_table record as done, and next... You can write this as a stored procedure, and it can convert your data in the background, eating minimal resources...
Or another (probably better):
create fact_table as EVERY record from raw_fact_table AND one dimension_id. (so including dimension_text and dimension_id rows)
create dimension_table
create an after insert trigger for fact_table which:
searches for dimension_text in fact_table
if not found, creates a new record in dimension_table
updates dimension_id to this id
in a simle loop, insert every record from raw_fact_table to fact_table
You are omitting some details there at the end, but I don't see that there necessarily is a problem. It is not in evidence that all strings are actually compared to all other strings. If you do a join, PostgreSQL could very well pick a smarter join algorithm, such as a hash join, which might give you the same hashing that you are implementing yourself in your MySQL solution. (Again, your details are hazy on that.)
-- add unique index
CREATE UNIQUE INDEX uidx ON dimension_table USING hash(text);
-- for non case-sensitive hash(upper(text))
try hash(text); and btree(text) to see which one is faster
I an see several ways of solving your problem
There is md5 function in PostgreSql
md5(string) Calculates the MD5 hash of string, returning the result in hexadecimal
insert into dimension_table (select null, md5(text), text from raw_fact_table group by text)
add md5 field into raw_fact_table as well
select id into fact_table from dimension inner join raw_fact_table on (dimension.md5 = raw_fact_table.md5);
Indexes on MD5 filed might help as well
Or you can calculate MD5 on the fly while loading the data.
For example our ETL tool Advanced ETL processor can do it for you.
Plus it can load data into multiple tables same time.
There is a number of on-line tutorials available on our web site
For example this one demonstrates loading slow changing dimension
http://www.dbsoftlab.com/online-tutorials/advanced-etl-processor/advanced-etl-processor-working-with-slow-changing-dimension-part-2.html
In which situations you should use inherited tables? I tried to use them very briefly and inheritance didn't seem like in OOP world.
I thought it worked like this:
Table users has all fields required for all user levels. Tables like moderators, admins, bloggers, etc but fields are not checked from parent. For example users has email field and inherited bloggers has it now too but it's not unique for both users and bloggers at the same time. ie. same as I add email field to both tables.
The only usage I could think of is fields that are usually used, like row_is_deleted, created_at, modified_at. Is this the only usage for inherited tables?
There are some major reasons for using table inheritance in postgres.
Let's say, we have some tables needed for statistics, which are created and filled each month:
statistics
- statistics_2010_04 (inherits statistics)
- statistics_2010_05 (inherits statistics)
In this sample, we have 2.000.000 rows in each table. Each table has a CHECK constraint to make sure only data for the matching month gets stored in it.
So what makes the inheritance a cool feature - why is it cool to split the data?
PERFORMANCE: When selecting data, we SELECT * FROM statistics WHERE date BETWEEN x and Y, and Postgres only uses the tables, where it makes sense. Eg. SELECT * FROM statistics WHERE date BETWEEN '2010-04-01' AND '2010-04-15' only scans the table statistics_2010_04, all other tables won't get touched - fast!
Index size: We have no big fat table with a big fat index on column date. We have small tables per month, with small indexes - faster reads.
Maintenance: We can run vacuum full, reindex, cluster on each month table without locking all other data
For the correct use of table inheritance as a performance booster, look at the postgresql manual.
You need to set CHECK constraints on each table to tell the database, on which key your data gets split (partitioned).
I make heavy use of table inheritance, especially when it comes to storing log data grouped by month. Hint: If you store data, which will never change (log data), create or indexes with CREATE INDEX ON () WITH(fillfactor=100); This means no space for updates will be reserved in the index - index is smaller on disk.
UPDATE:
fillfactor default is 100, from http://www.postgresql.org/docs/9.1/static/sql-createtable.html:
The fillfactor for a table is a percentage between 10 and 100. 100 (complete packing) is the default
"Table inheritance" means something different than "class inheritance" and they serve different purposes.
Postgres is all about data definitions. Sometimes really complex data definitions. OOP (in the common Java-colored sense of things) is about subordinating behaviors to data definitions in a single atomic structure. The purpose and meaning of the word "inheritance" is significantly different here.
In OOP land I might define (being very loose with syntax and semantics here):
import life
class Animal(life.Autonomous):
metabolism = biofunc(alive=True)
def die(self):
self.metabolism = False
class Mammal(Animal):
hair_color = color(foo=bar)
def gray(self, mate):
self.hair_color = age_effect('hair', self.age)
class Human(Mammal):
alcoholic = vice_boolean(baz=balls)
The tables for this might look like:
CREATE TABLE animal
(name varchar(20) PRIMARY KEY,
metabolism boolean NOT NULL);
CREATE TABLE mammal
(hair_color varchar(20) REFERENCES hair_color(code) NOT NULL,
PRIMARY KEY (name))
INHERITS (animal);
CREATE TABLE human
(alcoholic boolean NOT NULL,
FOREIGN KEY (hair_color) REFERENCES hair_color(code),
PRIMARY KEY (name))
INHERITS (mammal);
But where are the behaviors? They don't fit anywhere. This is not the purpose of "objects" as they are discussed in the database world, because databases are concerned with data, not procedural code. You could write functions in the database to do calculations for you (often a very good idea, but not really something that fits this case) but functions are not the same thing as methods -- methods as understood in the form of OOP you are talking about are deliberately less flexible.
There is one more thing to point out about inheritance as a schematic device: As of Postgres 9.2 there is no way to reference a foreign key constraint across all of the partitions/table family members at once. You can write checks to do this or get around it another way, but its not a built-in feature (it comes down to issues with complex indexing, really, and nobody has written the bits necessary to make that automatic). Instead of using table inheritance for this purpose, often a better match in the database for object inheritance is to make schematic extensions to tables. Something like this:
CREATE TABLE animal
(name varchar(20) PRIMARY KEY,
ilk varchar(20) REFERENCES animal_ilk NOT NULL,
metabolism boolean NOT NULL);
CREATE TABLE mammal
(animal varchar(20) REFERENCES animal PRIMARY KEY,
ilk varchar(20) REFERENCES mammal_ilk NOT NULL,
hair_color varchar(20) REFERENCES hair_color(code) NOT NULL);
CREATE TABLE human
(mammal varchar(20) REFERENCES mammal PRIMARY KEY,
alcoholic boolean NOT NULL);
Now we have a canonical reference for the instance of the animal that we can reliably use as a foreign key reference, and we have an "ilk" column that references a table of xxx_ilk definitions which points to the "next" table of extended data (or indicates there is none if the ilk is the generic type itself). Writing table functions, views, etc. against this sort of schema is so easy that most ORM frameworks do exactly this sort of thing in the background when you resort to OOP-style class inheritance to create families of object types.
Inheritance can be used in an OOP paradigm as long as you do not need to create foreign keys on the parent table. By example, if you have an abstract class vehicle stored in a vehicle table and a table car that inherits from it, all cars will be visible in the vehicle table but a foreign key from a driver table on the vehicle table won't match theses records.
Inheritance can be also used as a partitionning tool. This is especially usefull when you have tables meant to be growing forever (log tables etc).
Main use of inheritance is for partitioning, but sometimes it's useful in other situations. In my database there are many tables differing only in a foreign key. My "abstract class" table "image" contains an "ID" (primary key for it must be in every table) and PostGIS 2.0 raster. Inherited tables such as "site_map" or "artifact_drawing" have a foreign key column ("site_name" text column for "site_map", "artifact_id" integer column for the "artifact_drawing" table etc.) and primary and foreign key constraints; the rest is inherited from the the "image" table. I suspect I might have to add a "description" column to all the image tables in the future, so this might save me quite a lot of work without making real issues (well, the database might run little slower).
EDIT: another good use: with two-table handling of unregistered users, other RDBMSs have problems with handling the two tables, but in PostgreSQL it is easy - just add ONLY when you are not interrested in data in the inherited "unregistered user" table.
The only experience I have with inherited tables is in partitioning. It works fine, but it's not the most sophisticated and easy to use part of PostgreSQL.
Last week we were looking the same OOP issue, but we had too many problems with Hibernate - we didn't like our setup, so we didn't use inheritance in PostgreSQL.
I use inheritance when I have more than 1 on 1 relationships between tables.
Example: suppose you want to store object map locations with attributes x, y, rotation, scale.
Now suppose you have several different kinds of objects to display on the map and each object has its own map location parameters, and map parameters are never reused.
In these cases table inheritance would be quite useful to avoid having to maintain unnormalised tables or having to create location id’s and cross referencing it to other tables.
I tried some operations on it, I will not point out if is there any actual use case for database inheritance, but I will give you some detail for making your decision. Here is an example of PostgresQL: https://www.postgresql.org/docs/15/tutorial-inheritance.html
You can try below SQL script.
CREATE TABLE IF NOT EXISTS cities (
name text,
population real,
elevation int -- (in ft)
);
CREATE TABLE IF NOT EXISTS capitals (
state char(2) UNIQUE NOT NULL
) INHERITS (cities);
ALTER TABLE cities
ADD test_id varchar(255); -- Both table would contains test col
DROP TABLE cities; -- Cannot drop because capitals depends on it
ALTER TABLE cities
ADD CONSTRAINT fk_test FOREIGN KEY (test_id) REFERENCES sometable (id);
As you can see my comments, let me summarize:
When you add/delete/update fields -> the inheritance table would also be affected.
Cannot drop the parent table.
Foreign keys would not be inherited.
From my perspective, in growing applications, we cannot easily predict the changes in the future, for me I would avoid applying this to early database developing.
When features are stable as well and we want to create some database model which much likely the same as the existing one, we can consider that use case.
Use it as little as possible. And that usually means never, it boiling down to a way of creating structures that violate the relational model, for instance by breaking the information principle and by creating bags instead of relations.
Instead, use table partitioning combined with proper relational modelling, including further normal forms.