For simplicity, say there are two tables A and B, with unique indexes on non-key INT columns COLUMN_A and COLUMN_B respectively. I would like to do something like the following pseudo-SQL. (Note that this would be in a stored procedure but, according to https://mariadb.com/kb/en/lock-tables/, that's not allowed.)
IF COLUMN_A != x for all rows of A
Single query to insert a row into B with COLUMN_B = x (if not exist)
The problem is that other parts of the code could read A and B, see that x does not exist in either, and try to insert x into A between the IF statement and insertion query. The race condition seems to necessitate a read/write lock on A. I don't think that InnoDB's internal locking would prevent this from happening (i.e. any locks used during the IF statement would be released before the execution of the insertion).
Crucially, COLUMN_A and COLUMN_B are formally unrelated so there doesn't appear to be a straightforward way to enforce a uniqueness constraint between them (given that a view involving both A and B probably wouldn't be updatable). (I would be fine creating some sort of "relationship" between them as long as they remain in separate tables but I'm not sure if there is anything that would do this.) Is it necessary to have a table lock on A in this case?
Something like this would seem like a better solution: Can I use row locks on rows that have not been created yet? But this question is about Postgres and the features don't appear to be available in MySQL.
Thank you.
Put the condition into the INSERT statement rather than using the IF statement.
INSERT INTO B (col1, col2, col3, ...)
SELECT val1, val2, val3, ...
FROM DUAL
WHERE NOT EXISTS (
SELECT *
FROM A
WHERE column_a = 'x'
)
I have a MySQL database with 2 tables suppose table1 & table2 each having the same set of 300 columns.
Now what I want to do is create a new table (eg. table3) consisting of rows from table1 and table2.
There is no row matching needed between the two tables. I tried insert, union and various other operations but the catch is the columns are unordered inside the tables. But they have the same set of column names.
I want to merge the two tables with the rows having data in respective columns, which I am not able to do using UNION or INSERT operation.
You can always explicitly specify column names instead of insert into t values and select *:
insert into t3(col1, col2, col3, ...)
select col1, col2, col3, ... from t1
union all
select col1, col2, col3, ... from t2
After trying a lot of different ways to merge the tables using SQL commands I was unable to find any solution which fulfilled the requirements.
One way in which this can be solved using Python is to load the tables into a Pandas dataframe and then concat these two to a new dataframe.
Pandas merge them properly by matching the column names and then you can reload the table to the database.
Any other solution is appreciated.
Sample Table:
+----+-------+-------+-------+-------+-------+---------------+
| id | col1 | col2 | col3 | col4 | col5 | modifiedTime |
+----+-------+-------+-------+-------+-------+---------------+
| 1 | temp1 | temp2 | temp3 | temp4 | temp5 | 1554459626708 |
+----+-------+-------+-------+-------+-------+---------------+
above table has 50 million records
(col1, col2, col3, col4, col5 these are VARCHAR columns)
(id is PK)
(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.
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.
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.
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.
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.
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.
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
...
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
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%]
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.
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.
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
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.
so I have written a query as follows:
UPDATE
table1 latest, table2 previous
SET latest.col1 = previous.col1
WHERE latest.col2 = previous.col2 and previous.col1 is not null;
which copies the value of col2, from table2 to table 1 wherever the value of col1, matches. However due to the context, there can be no primary/foreign key constraints involve and col2 doesn't contain nulls but col1 does( in both tables)..
however this query takes several minutes to execute! is there a way to speeded it up?
Fixed by adding indexes to both table. Created indexes for the common column on which the tables were being joined. Wherever lookups are performed, either through joins, the columns being joined/lookedup should have indexes
Been searching on Google for a while now without finding the answer to my problem. I have like 10 tables where 5 of them contains 150 rows. I want to add 15 rows to these 5 tables, is there any simple solution for this? I know it's easy to add the rows manually but I want to know anyway. What I'm looking for is something like this:
INSERT INTO all_tables VALUES (col1, col2, col3) WHERE row_number() = '150'
Is it possible? Thanks in advance!
You can only target updates to one table at a time, which must always be specified by name. Also, you cannot specify a WHERE clause on an INSERT. Your best bet is probably to write one INSERT and copy and paste for the rest.
You could:
Loop through a list of the relevant table names.
Run a dynamic query like select count(*) into #c1 from SpecifiedTable against the relevant table, returning the count into a declared variable.
If the returned value is 150, run another dynamic query to insert the relevant values into the specified table.
You can find out more about dynamic queries and returning values from them in MySQL here. If this is a once-off, you will probably find it easier to do it manually.