I have a couple of tables that I need to join. I have an employees table (~ 400K rows), a companies table (~10 million rows) and a employee_companies table which stores where someone works.

Basically, I need to get all the employees that match some conditions (they work on a company that has a website, are located in a certain country, etc). I made a query to get this, but it's taking too long. I need to speed it up.

SELECT  DISTINCT "employees".* 
FROM "employees" 
INNER JOIN "employee_companies" ON "employee_companies"."employee_id" = "employees"."id" 
INNER JOIN "companies" ON "companies"."id" = "employee_companies"."company_id" 
WHERE (employee_companies.employee_id IS NOT NULL)
AND (companies.website IS NOT NULL) 
AND (employees.country = 'Uruguay') 
ORDER BY employees.connections DESC

This is the plan for that query:

Unique  (cost=877170.24..880752.72 rows=62304 width=1064) (actual time=24023.736..26001.876 rows=73318 loops=1)
  ->  Sort  (cost=877170.24..877326.00 rows=62304 width=1064) (actual time=24023.733..24305.989 rows=77579 loops=1)
        Sort Key: employees.connections DESC, employees.id, employees.name, employees.link, employees.role, employees.area, employees.profile_picture, employees.summary, employees.current_companies, employees.previous_companies, employees.skills, employees.education, employees.languages, employees.volunteer, employees.groups, employees.interests, employees.search_vector, employees.secondary_search_vector, employees.email_status, employees.languages_count, employees.role_hierarchy
        Sort Method: external merge  Disk: 85816kB
        ->  Nested Loop  (cost=2642.38..843246.15 rows=62304 width=1064) (actual time=139.870..23056.234 rows=77579 loops=1)
              ->  Hash Join  (cost=2641.95..221744.50 rows=77860 width=1068) (actual time=139.841..22617.587 rows=77579 loops=1)
                    Hash Cond: (employees.id = employee_companies.employee_id)
                    ->  Seq Scan on employees  (cost=0.00..212178.88 rows=409672 width=1064) (actual time=8.145..22369.166 rows=393725 loops=1)
                          Filter: ((country)::text = 'Uruguay'::text)
                          Rows Removed by Filter: 1075
                    ->  Hash  (cost=1666.42..1666.42 rows=78042 width=8) (actual time=44.675..44.675 rows=78042 loops=1)
                          Buckets: 131072  Batches: 1  Memory Usage: 4073kB
                          ->  Seq Scan on employee_companies  (cost=0.00..1666.42 rows=78042 width=8) (actual time=0.007..22.901 rows=78042 loops=1)
                                Filter: (employee_id IS NOT NULL)
              ->  Index Scan using companies_pkey on companies  (cost=0.43..7.97 rows=1 width=4) (actual time=0.004..0.004 rows=1 loops=77579)
                    Index Cond: (id = employee_companies.company_id)
                    Filter: (website IS NOT NULL)
Planning time: 1.957 ms
Execution time: 26025.045 ms

And these are the relevant indexes that I have on my table:


"employees_pkey" PRIMARY KEY, btree (id)
"ix_employees_country" btree (country)


"companies_pkey" PRIMARY KEY, btree (id)
"empty_websites" btree (website) WHERE website IS NULL
"index_companies_on_website" btree (website)
"not_empty_websites" btree (website) WHERE website IS NOT NULL


"employee_companies_pkey" PRIMARY KEY, btree (id)
"index_employee_companies_on_company_id" btree (company_id)
"index_employee_companies_on_employee_id" btree (employee_id)
"index_employee_companies_on_employee_id_and_company_id" btree (employee_id, company_id)
"not_empty_employee_id" btree (employee_id) WHERE employee_id IS NOT NULL

Is there any other better way to do what I want that is more efficient/performant?


  • 1
    index_employee_companies_on_employee_id is useless you already have that index with index_employee_companies_on_employee_id_and_company_id
    – Blag
    Apr 25 '17 at 20:10
  • 2
    You "employee_companies" doesn't need an anonymous id as a PKEY. You can use (company_id, employee_id) as the PKEY. That already guarantees that employee_id is not null. It wouldn't make sense for an intermediate table representing a many-to-many relationship to have null values in either column. You might need a second index (employee_id, company_id), but nothing more. That won't speed up your query, but will speed up inserting values on the table.
    – joanolo
    Apr 25 '17 at 20:10
  • @joanolo yes you are right! my mistake. I have 394800 employees on the database, but 73116 are the ones that I care about (the ones that work for a company that has a website). Sorry for that.
    – jpbalarini
    Apr 25 '17 at 21:13
  • remove the useless employee_id IS NOT NULL and update your explain, you're messing with the query optimizer for something that can't survive your inner join anyway
    – Blag
    Apr 25 '17 at 21:28

Based on some guess-simulations, I think you can slightly improve your query by:

  1. Avoiding the outer DISTINCT clause (although there will be an implicitly DISTINCT).
  2. Sub-selecting a part of the data so that less is needed to JOIN.

The query is as follows:

    employee_id IN
        -- Choose all employees from companies with website
        JOIN companies ON companies.company_id = employee_companies.company_id
        companies.website IS NOT NULL
    -- Now filter only employees from 'Germany'
    AND employees.country = 'Germany' 
    employees.connections DESC ;

The data used to produce the simulation is the following one:

Table and index definitions:

CREATE TABLE employees
    employee_id integer PRIMARY KEY,
    country text,
    connections integer,
    something_else text
) ;

CREATE INDEX idx_employee_country 
   ON employees (country) ;

CREATE TABLE companies
    company_id integer PRIMARY KEY,
    website text,
    something_else text
) ;

CREATE INDEX not_empty_websites 
    ON companies(company_id, website) WHERE website IS NOT NULL ;

CREATE TABLE employee_companies
    employee_id integer NOT NULL REFERENCES employees(employee_id),
    company_id integer NOT NULL REFERENCES companies(company_id),
    PRIMARY KEY (employee_id, company_id)
) ;

CREATE INDEX company_employee
    ON employee_companies(company_id, employee_id) ;

1.000.000 companies (changing to 10M doesn't make a big difference). I assume 90% have a website.

   (company_id, website)
   generate_series(1, 1000000), 
   CASE WHEN random() > 0.1 THEN 'web.com' END AS website ;

80k employees (about 10% are Germans)

   (employee_id, country, connections)
    generate_series(1, 80000),
    case (random()*10)::integer
    when 0 then 'Germany'
    when 1 then 'United Kingdon'
    when 2 then 'United States'
    else 'Angola'
    end AS country,
    (random()*10)::integer AS connections ;

200K employees x companies (this means that people have worked in about 3 companies, on average):

    (employee_id, company_id)
    (random()*79999)::integer + 1,
    (random()*999999)::integer + 1
    generate_series (1, 200000) ;

You can check a downsized version of this simulation at dbfiddle here. If this simulated data is sufficiently similar to your scenario, changing the query makes a 3x improvement with regard to server-execution time. I'd suggest you give it a try.

Simulating data (scaled down by a factor of 25) a scenario more similar to your real one doesn't offer such a nice increase in performance... Nevertheless, it improves by a 1.5 factor.

Check it at this dbfiddle


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.