How to get Count for large tables?

Question

Sample Table:

+----+-------+-------+-------+-------+-------+---------------+
| id | col1  | col2  | col3  | col4  | col5  | modifiedTime  |
+----+-------+-------+-------+-------+-------+---------------+
|  1 | temp1 | temp2 | temp3 | temp4 | temp5 | 1554459626708 |
+----+-------+-------+-------+-------+-------+---------------+ 

above table has 50 million records

1. (col1, col2, col3, col4, col5 these are VARCHAR columns)
2. (id is PK)
3. (modifiedTime)

Every column is indexed

For Ex: I have two tabs in my website.

FirstTab - I print the count of above table with following criteria [col1 like "value1%" and col2 like "value2%"]

SeocndTab - I print the count of above table with following criteria [col3 like "value3%"]

---

As I have 50 million records, the count with those criteria takes too much time to get the result.

Note: I would change records data(rows in table) sometime. Insert new rows. Delete not needed records.

I need a feasible solution instead of querying the whole table. Ex: like caching the older count. Is anything like this possible.

Answer 1

While I'm sure it's possible for MySQL, here's a solution for Postgres, using triggers.

Count is stored in another table, and there's a trigger on each insert/update/delete that checks if the new row meets the condition(s), and if it does, add 1 to the count. Another part of the trigger checks if the old row meets the condition(s), and if it does, subtracts 1.

Here's the basic code for the trigger that counts the rows with temp2 = '5':

CREATE OR REPLACE FUNCTION updateCount() RETURNS TRIGGER AS 
$func$
BEGIN
   IF TG_OP = 'INSERT' OR TG_OP = 'UPDATE' THEN
      EXECUTE 'UPDATE someTableCount SET cnt = cnt + 1 WHERE 1 = (SELECT 1 FROM (VALUES($1.*)) x(id, temp1, temp2, temp3) WHERE x.temp2 = ''5'')'
      USING NEW;
   END IF;
   IF TG_OP = 'DELETE' OR TG_OP = 'UPDATE' THEN
      EXECUTE 'UPDATE someTableCount SET cnt = cnt - 1 WHERE 1 = (SELECT 1 FROM (VALUES($1.*)) x(id, temp1, temp2, temp3) WHERE x.temp2 = ''5'')'
      USING OLD;
   END IF;
   RETURN new;
END
$func$ LANGUAGE plpgsql;

Here's a working example on dbfiddle.

You could of course modify the trigger code to have dynamic where expressions and store counts for each in the table like:

CREATE TABLE someTableCount
(
   whereExpr text,
   cnt INT
);

INSERT INTO someTableCount VALUES ('temp2 = ''5''', 0);

In the trigger you'd then loop through the conditions and update accordingly.

Answer 2

FirstTab - I print the count of above table with following criteria [col1 like "value1%" and col2 like "value2%"]

That would benefit from a 'composite' index:

INDEX(col1, col2)

because it would be "covering". (That is, all the columns needed in the query are found in a single index.)

SeocndTab - I print the count of above table with following criteria [col3 like "value3%"]

You apparently already have the optimal (covering) index:

INDEX(col3)

Now, let's look at it from a different point of view. Have you noticed that search engines no longer give you an exact count of rows that match? You are finding out why -- It takes too long to do the tally not matter what technique is used.

Since "col1" gives me no clue of your app, nor any idea of what is being counted, I can only throw out some generic recommendations:

• Don't give the counts.
• Precompute the counts, save them somewhere and deliver 'stale' values. This can be handy if there are only a few different "values" being counted. It is probably not practical for arbitrary strings.
• Say "about nnnn" in the output.
• Play some tricks to decide whether it is practical to compute the exact value or just say "about".
• Say "more than 1000".
• etc

If you would like to describe the app and the columns, perhaps I can provide some clever tricks.

You expressed concern about "insert speed". This is usually not an issue, and the benefit of having the 'right' index for SELECTs outweighs the slight performance hit for INSERTs.

Answer 3

It sounds like you're trying to use a hammer when a screwdriver is needed. If you don't want to run batch computations, I'd suggest using a streaming framework such as Flink or Samza to add and subtract from your counts when records are added or deleted. This is precisely what those frameworks are built for.

If you're committed to using SQL, you can set up a job that performs the desired count operations every given time window, and stores the values to a second table. That way you don't have to perform repeated counts across the same rows.

Answer 4

As a general rule of thumb when it comes to optimisation (and yes, 1 SQL server node@50mio entries per table needs one!), here is a list of few possible optimisation techniques, some fairly easy to implement, others maybe need more serious modifications:

• optimize your MYSQL field type and sizes, eg. use INT instead of VARCHAR if data can be presented with numbers, use SMALL INT instead of BIG INT, etc. In case you really need to have VARCHAR, then use as small as possible length of each field,

• look at your dataset; is there any repeating values? Let say if any of your field has only 5 unique values in 50mio rows, then save those values to separate table and just link PK to this Sample Table,

• MYSQL partitioning, basic understanding is shown at this link, so the general idea is so implement some kind of partitioning scheme, e.g. new partition is created by CRONJOB every day at "night" when server utilization is at minimum, or when you reach another 50k INSERTs or so (btw also some extra effort will be needed for UPDATE/DELETE operations on different partitions),

• caching is another very simple and effective approach, since requesting (almost) same data (I am assuming your value1%, value2%, value3% are always the same?) over and over again. So do SELECT COUNT() once a while, and then use differencial index count to get actual number of selected rows,

• in-memory database can be used alongside tradtional SQL DBs to get often-needed data: simple key-value pair style could be enough: Redis, Memcached, VoltDB, MemSQL are just some of them. Also, MYSQL also knows in-memory engine,

• use other types of DBs, e.g NoSQL DB like MongoDB, if your dataset/system can utilize different concept.

Answer 5

If you are looking for aggregation performance and don't really care about insert times, I would consider changing your Row DBMS for a Column DBMS.

A Column RDBMS stores data as columns, meaning each column is indexed independantly from the others. This allows way faster aggregations, I have switched from Postgres to MonetDB (an open source column DBMS) and summing one field from a 6 milions lines table dropped down from ~60s to 50ms. I chose MonetDB as it supports SQL querying and odbc connections which were a plus for my use case, but you will experience similar performance improvements with other Column DBMS.

There is a downside to Column storing, which is that you lose performance on insert, update and delete queries, but from what you said, I believe it won't affect you that much.

Answer 6

this will work:

select count(*) from (
select * from tablename where col1 like 'value1%' and col2 like 'value2%' and col3 
like'value3%')
where REGEXP_LIKE(col1,'^value1(.*)$') and REGEXP_LIKE(col2,'^value2(.*)$') and 
REGEXP_LIKE(col1,'^value2(.*)$');

try not to apply index on all the columns as it slows down the processing of a sql query and have it in required columns only.

Answer 7

In Postgres, you can get an estimated row count from the internal statistics that are managed by the query planner:

SELECT reltuples AS approximate_row_count FROM pg_class WHERE relname = 'mytable';

Here you have more details: https://wiki.postgresql.org/wiki/Count_estimate

You could create a materialized view first. Something like this:

CREATE MATERIALIZED VIEW mytable AS SELECT * FROM the_table WHERE col1 like "value1%" and col2 like "value2%";`

You can also materialize directly the count queries. If you have 10 tabs, then you should have to materialize 10 views:

CREATE MATERIALIZED VIEW count_tab1 AS SELECT count(*) FROM the_table WHERE col1 like "value1%" and col2 like "value2%";`
CREATE MATERIALIZED VIEW count_tab2 AS SELECT count(*) FROM the_table WHERE col2 like "value2%" and col3 like "value3%";`
...

After each insert, you should refresh views (asynchronously):

REFRESH MATERIALIZED VIEW count_tab1
REFRESH MATERIALIZED VIEW count_tab2
...

Answer 8

As noted in the critique, you have not posted what you have tried. So I would assume that the limit of question is exactly what you posted. So kindly report results of exactly that much

1. What is the current time you are spending for the subset of the problem, i.e. count of [col1 like "value1%" and col2 like "value2%"] and 2nd [col3 like "value3%]
2. The trick would be to scan the data source once and make the data source smaller by creating an index. So first create an index on col1,col2,col3,id. Purpose of col3 and id is so that database scans just the index. And I would get both counts in same SQL

select sum
       (
           case 
               when col1 like 'value1%' and col2 like 'value2%' then 1
               else 0
           end
       ) cnt_condition_1,
       sum
       (
           case 
               when col3 like 'value3%' then 1
               else 0
           end
       ) cnt_condition_2
from table
where (col1 like 'value1%' and col2 like 'value2%') or
      (col3 like 'value3%')
```
So the 50M row table is probably very wide right now.  This should trim it down - on a reasonable server I would expect above to return in a few seconds.  If it does not and each condition returns < 10% of the table, second option will be to create multiple indexes for each scenario and do count for each so that index is used in each case.

Answer 9

If there is no bulk insert/ bulk updates happening in your system, Can you try vertical partitioning in your table? By vertical partitioning, you can separate the data block of col1, col2 from other data of the table and so your searching space will reduce.

Also, indexing on every columns doesn't seem to be the best approach to go with. Index wherever it is absolutely needed. In this case, I would say Index(col1,col2) and Index(col3).

Even after indexing, you need to look into the fragmentation of those indexes and modify it accordingly to get the best results. Because, sometimes 50 million index of one column can sit as one huge chunk, which will restrict multi processing capabilities of your SQL server.

Answer 10

Each Database has their own peculiarities in how to "enhance" their RDBMS. I can't speak for MySQL or SQL Server but for PostgreSQL you should consider making the indexes that you search as GIN (Generalized Inverted Index)-based indexes.

CREATE INDEX name ON table USING gin(col1);
CREATE INDEX name ON table USING gin(col2);
CREATE INDEX name ON table USING gin(col3);

More information can be found here.

-HTH