I need to programmatically insert tens of millions of records into a Postgres database. Presently, I'm executing thousands of insert statements in a single query.
Is there a better way to do this, some bulk insert statement I do not know about?
PostgreSQL has a guide on how to best populate a database initially, and they suggest using the COPY command for bulk loading rows. The guide has some other good tips on how to speed up the process, like removing indexes and foreign keys before loading the data (and adding them back afterwards).
There is an alternative to using COPY, which is the multirow values syntax that Postgres supports. From the documentation:
INSERT INTO films (code, title, did, date_prod, kind) VALUES
('B6717', 'Tampopo', 110, '1985-02-10', 'Comedy'),
('HG120', 'The Dinner Game', 140, DEFAULT, 'Comedy');
The above code inserts two rows, but you can extend it arbitrarily, until you hit the maximum number of prepared statement tokens (it might be $999, but I'm not 100% sure about that). Sometimes one cannot use COPY, and this is a worthy replacement for those situations.
One way to speed things up is to explicitly perform multiple inserts or copy's within a transaction (say 1000). Postgres's default behavior is to commit after each statement, so by batching the commits, you can avoid some overhead. As the guide in Daniel's answer says, you may have to disable autocommit for this to work. Also note the comment at the bottom that suggests increasing the size of the wal_buffers to 16 MB may also help.
UNNEST function with arrays can be used along with multirow VALUES syntax. I'm think that this method is slower than using COPY but it is useful to me in work with psycopg and python (python list passed to cursor.execute becomes pg ARRAY):
INSERT INTO tablename (fieldname1, fieldname2, fieldname3)
VALUES (
UNNEST(ARRAY[1, 2, 3]),
UNNEST(ARRAY[100, 200, 300]),
UNNEST(ARRAY['a', 'b', 'c'])
);
without VALUES using subselect with additional existance check:
INSERT INTO tablename (fieldname1, fieldname2, fieldname3)
SELECT * FROM (
SELECT UNNEST(ARRAY[1, 2, 3]),
UNNEST(ARRAY[100, 200, 300]),
UNNEST(ARRAY['a', 'b', 'c'])
) AS temptable
WHERE NOT EXISTS (
SELECT 1 FROM tablename tt
WHERE tt.fieldname1=temptable.fieldname1
);
the same syntax to bulk updates:
UPDATE tablename
SET fieldname1=temptable.data
FROM (
SELECT UNNEST(ARRAY[1,2]) AS id,
UNNEST(ARRAY['a', 'b']) AS data
) AS temptable
WHERE tablename.id=temptable.id;
((this is a WIKI you can edit and enhance the answer!))
The external file is the best and typical bulk-data
The term "bulk data" is related to "a lot of data", so it is natural to use original raw data, with no need to transform it into SQL. Typical raw data files for "bulk insert" are CSV and JSON formats.
Bulk insert with some transformation
In ETL applications and ingestion processes, we need to change the data before inserting it. Temporary table consumes (a lot of) disk space, and it is not the faster way to do it. The PostgreSQL foreign-data wrapper (FDW) is the best choice.
CSV example. Suppose the tablename (x, y, z) on SQL and a CSV file like
fieldname1,fieldname2,fieldname3
etc,etc,etc
... million lines ...
You can use the classic SQL COPY to load (as is original data) into tmp_tablename, them insert filtered data into tablename... But, to avoid disk consumption, the best is to ingested directly by
INSERT INTO tablename (x, y, z)
SELECT f1(fieldname1), f2(fieldname2), f3(fieldname3) -- the transforms
FROM tmp_tablename_fdw
-- WHERE condictions
;
You need to prepare database for FDW, and instead static tmp_tablename_fdw you can use a function that generates it:
CREATE EXTENSION file_fdw;
CREATE SERVER import FOREIGN DATA WRAPPER file_fdw;
CREATE FOREIGN TABLE tmp_tablename_fdw(
...
) SERVER import OPTIONS ( filename '/tmp/pg_io/file.csv', format 'csv');
JSON example. A set of two files, myRawData1.json and Ranger_Policies2.json can be ingested by:
INSERT INTO tablename (fname, metadata, content)
SELECT fname, meta, j -- do any data transformation here
FROM jsonb_read_files('myRawData%.json')
-- WHERE any_condiction_here
;
where the function jsonb_read_files() reads all files of a folder, defined by a mask:
CREATE or replace FUNCTION jsonb_read_files(
p_flike text, p_fpath text DEFAULT '/tmp/pg_io/'
) RETURNS TABLE (fid int, fname text, fmeta jsonb, j jsonb) AS $f$
WITH t AS (
SELECT (row_number() OVER ())::int id,
f AS fname,
p_fpath ||'/'|| f AS f
FROM pg_ls_dir(p_fpath) t(f)
WHERE f LIKE p_flike
) SELECT id, fname,
to_jsonb( pg_stat_file(f) ) || jsonb_build_object('fpath', p_fpath),
pg_read_file(f)::jsonb
FROM t
$f$ LANGUAGE SQL IMMUTABLE;
Lack of gzip streaming
The most frequent method for "file ingestion" (mainlly in Big Data) is preserving original file on gzip format and transfering it with streaming algorithm, anything that can runs fast and without disc consumption in unix pipes:
gunzip remote_or_local_file.csv.gz | convert_to_sql | psql
So ideal (future) is a server option for format .csv.gz.
Note after #CharlieClark comment: currently (2022) nothing to do, the best alternative seems pgloader STDIN:
gunzip -c file.csv.gz | pgloader --type csv ... - pgsql:///target?foo
You can use COPY table TO ... WITH BINARY which is "somewhat faster than the text and CSV formats." Only do this if you have millions of rows to insert, and if you are comfortable with binary data.
Here is an example recipe in Python, using psycopg2 with binary input.
It mostly depends on the (other) activity in the database. Operations like this effectively freeze the entire database for other sessions. Another consideration is the datamodel and the presence of constraints,triggers, etc.
My first approach is always: create a (temp) table with a structure similar to the target table (create table tmp AS select * from target where 1=0), and start by reading the file into the temp table.
Then I check what can be checked: duplicates, keys that already exist in the target, etc.
Then I just do a do insert into target select * from tmp or similar.
If this fails, or takes too long, I abort it and consider other methods (temporarily dropping indexes/constraints, etc)
I just encountered this issue and would recommend csvsql (releases) for bulk imports to Postgres. To perform a bulk insert you'd simply createdb and then use csvsql, which connects to your database and creates individual tables for an entire folder of CSVs.
$ createdb test
$ csvsql --db postgresql:///test --insert examples/*.csv
I implemented very fast Postgresq data loader with native libpq methods.
Try my package https://www.nuget.org/packages/NpgsqlBulkCopy/
May be I'm late already. But, there is a Java library called pgbulkinsert by Bytefish. Me and my team were able to bulk insert 1 Million records in 15 seconds. Of course, there were some other operations that we performed like, reading 1M+ records from a file sitting on Minio, do couple of processing on the top of 1M+ records, filter down records if duplicates, and then finally insert 1M records into the Postgres Database. And all these processes were completed within 15 seconds. I don't remember exactly how much time it took to do the DB operation, but I think it was around less then 5 seconds. Find more details from https://www.bytefish.de/blog/pgbulkinsert_bulkprocessor.html
As others have noted, when importing data into Postgres, things will be slowed by the checks that Postgres is designed to do for you. Also, you often need to manipulate the data in one way or another so that it's suitable for use. Any of this that can be done outside of the Postgres process will mean that you can import using the COPY protocol.
For my use I regularly import data from the httparchive.org project using pgloader. As the source files are created by MySQL you need to be able to handle some MySQL oddities such as the use of \N for an empty value and along with encoding problems. The files are also so large that, at least on my machine, using FDW runs out of memory. pgloader makes it easy to create a pipeline that lets you select the fields you want, cast to the relevant data types and any additional work before it goes into your main database so that index updates, etc. are minimal.
The query below can create test table with generate_series column which has 10000 rows. *I usually create such test table to test query performance and you can check generate_series():
CREATE TABLE test AS SELECT generate_series(1, 10000);
postgres=# SELECT count(*) FROM test;
count
-------
10000
(1 row)
postgres=# SELECT * FROM test;
generate_series
-----------------
1
2
3
4
5
6
-- More --
And, run the query below to insert 10000 rows if you've already had test table:
INSERT INTO test (generate_series) SELECT generate_series(1, 10000);
I have two databases in my SQL Server with each database containing 1 single table as of now.
I have 2 database like below :
1) Db1 (MySQL)
2) Db2 (Oracle)
Now what I want to do is fill my database table of SQL Server db1 with data from Db1 from MySQL like below :
Insert into Table1 select * from Table1
Select * from Table1(Mysql Db1) - Data coming from Mysql database
Insert into Table1(Sql server Db1) - Insert data coming from Mysql
database considering same schema
I don't want to use sqlbulk copy as I don't want to insert chunk by chunk data. I want to insert all data in 1 go considering millions of data as my operation is just not limited to insert records in database. So user have to sit wait for a long like first millions of data inserting chunk by chunk in database and then again for my further operation which is also long running operation.
So if I have this process speed up then I can have my second operation also speed up considering all records are in my 1 local sql server instance.
Is this possible to achieve in a C# application?
Update: I researched about Linked server as #GorDon Linoff suggested me that linked server can be use to achieve this scenario but based on my research it seems like i cannot create linked server through code.
I want to do this with the help of ado.net.
This is what I am trying to do exactly:
Consider I have 2 different client RDBMS with 2 database and some tables in client premises.
So database is like this :
Sql Server :
Db1
Order
Id Amount
1 100
2 200
3 300
4 400
Mysql or Oracle :
Db1:
Order
Id Amount
1 1000
2 2000
3 3000
4 400
Now I want to compare Amount column from source (SQL Server) to destination database (MySQL or Oracle).
I will be use to join this 2 different RDBMS databases tables to compare Amount columns.
In C# what I can do is like fetch chunk by chunk records in my datatable (in memory) then compare this records with the help of code but this will take so much time considering millions of records.
So I want to do something better than this.
Hence I was thinking that i bring out this 2 RDBMS records in my local SQL server instance in 2 databases and then create join query joining this 2 tables based on Id and then take advantage of DBMS processing capability which can compare this millions of records efficiently.
Query like this compares millions of records efficiently :
select SqlServer.Id,Mysql.Id,SqlServer.Amount,Mysql.Amount from SqlServerDb.dbo.Order as SqlServer
Left join MysqlDb.dbo.Order as Mysql on SqlServer.Id=Mysql.Id
where SqlServer.Amount != Mysql.Amount
Above query works when I have this 2 different RDBMS data in my local server instance with database : SqlServerDb and MysqlDb and this will fetch below records whose amount is not matching :
So I am trying to get those records from source(Sql server Db) to MySQL whose Amount column value is not matching.
Expected Output :
Id Amount
1 1000
2 2000
3 3000
So there is any way to achieve this scenario?
On the SELECT side, create a .csv file (tab-delimited) using SELECT ... INTO OUTFILE ...
On the INSERT side, use LOAD DATA INFILE ... (or whatever the target machine syntax is).
Doing it all at once may be easier to code than chunking, and may (or may not) be faster running.
SqlBulkCopy can accept either a DataTable or a System.Data.IDataReader as its input.
Using your query to read the source DB, set up a ADO.Net DataReader on the source MySQL or Oracle DB and pass the reader to the WriteToServer() method of the SqlBulkCopy.
This can copy almost any number of rows without limit. I have copied hundreds of millions of rows using the data reader approach.
What about adding a changed date in the remote database.
Then you could get all rows that have changed since the last sync and just compare those?
First of all do not use linked server. It is tempting but it will more trouble than it is bringing on the table. Like updates and inserts will fetch all of the target db to source db and do insert/update and post all data to target back.
As far as I understand you are trying to copy changed data to target system for some stuff.
I recommend using a timestamp column on source table. When anything changes on source table timestamp column is updated by sql server.
On target, get max ID and max timestamp. two queries at max.
On source, rows where source.ID <= target.MaxID && source.timestamp >= target.MaxTimeTamp is true, are the rows that changed after last sync (need update). And rows where source.ID > target.MaxID is true, are the rows that are inserted after last sync.
Now you do not have to compare two worlds, and you just got all updates and inserts.
You need to create a linked server connection using ODBC and the proper driver, after that you can execute the queries using openquery.
Take a look at openquery:
https://msdn.microsoft.com/en-us/library/ms188427(v=sql.120).aspx
Yes, SQL Server is very efficient when it's working with sets so let's keep that in play.
In a nutshell, what I'm pitching is
Load data from the source to a staging table on the target database (staging table = table to temporarily hold raw data from the source table, same structure as the source table... add tracking columns to taste). This will be done by your C# code... select from source_table into DataTable then SqlBulkCopy to the staging table.
Have a stored proc on the target database to reconcile the data between your target table and the staging table. Your C# code calls the stored proc.
Given that you're talking about millions of rows, another thing that can make things faster is dropping indices on the staging table before inserting to it and recreating those after the inserts and before any select is performed.
I'm looking for an efficient way of inserting records into SQL server for my C#/MVC application. Anyone know what the best method would be?
Normally I've just done a while loop and insert statement within, but then again I've not had quite so many records to deal with. I need to insert around half a million, and at 300 rows a minute with the while loop, I'll be here all day!
What I'm doing is looping through a large holding table, and using it's rows to create records in a different table. I've set up some functions for lookup data which is necessary for the new table, and this is no doubt adding to the drain.
So here is the query I have. Extremely inefficient for large amounts of data!
Declare #HoldingID int
Set #HoldingID = (Select min(HoldingID) From HoldingTable)
While #JourneyHoldingID IS NOT NULL
Begin
Insert Into Journeys (DepartureID, ArrivalID, ProviderID, JourneyNumber, Active)
Select
dbo.GetHubIDFromName(StartHubName),
dbo.GetHubIDFromName(EndHubName),
dbo.GetBusIDFromName(CompanyName),
JourneyNo, 1
From Holding
Where HoldingID = #HoldingID
Set #HoldingID = (Select MIN(HoldingID) From Holding Where HoldingID > #HoldingID)
End
I've heard about set-based approaches - is there anything that might work for the above problem?
If you want to insert a lot of data into a MSSQL Server then you should use BULK INSERTs - there is a command line tool called the bcp utility for this, and also a C# wrapper for performing Bulk Copy Operations, but under the covers they are all using BULK INSERT.
Depending on your application you may want to insert your data into a staging table first, and then either MERGE or INSERT INTO SELECT... to transfer those rows from the staging table(s) to the target table(s) - if you have a lot of data then this will take some time, however will be a lot quicker than performing the inserts individually.
If you want to speed this up then are various things that you can do such as changing the recovery model or tweaking / removing triggers and indexes (depending on whether or not this is a live database or not). If its still really slow then you should look into doing this process in batches (e.g. 1000 rows at a time).
This should be exactly what you are doing now.
Insert Into Journeys(DepartureID, ArrivalID, ProviderID, JourneyNumber, Active)
Select
dbo.GetHubIDFromName(StartHubName),
dbo.GetHubIDFromName(EndHubName),
dbo.GetBusIDFromName(CompanyName),
JourneyNo, 1
From Holding
ORDER BY HoldingID ASC
you (probably) are able to do it in one statement of the form
INSERT INTO JOURNEYS
SELECT * FROM HOLDING;
Without more information about your schema it is difficult to be absolutely sure.
SQLServer 2008 introduced Table Parameters. These allow you to insert multiple rows in a single trip to the database (send it as a large blob). Without using a temporary table. This article describes how it works (step four in the article)
http://www.altdevblogaday.com/2012/05/16/sql-server-high-performance-inserts/
It differs from bulk inserts in that you do not need special utilities and that all constraints and foreign keys are checked.
I quadrupled my throughput using this and parallelizing the inserts. Now at 15.000 inserts/second in the same table sustained. Regular table with indexes and over a billion rows.
I have a DataTable in memory that I need to dump straight into a SQL Server temp table.
After the data has been inserted, I transform it a little bit, and then insert a subset of those records into a permanent table.
The most time consuming part of this operation is getting the data into the temp table.
Now, I have to use temp tables, because more than one copy of this app is running at once, and I need a layer of isolation until the actual insert into the permanent table happens.
What is the fastest way to do a bulk insert from a C# DataTable into a SQL Temp Table?
I can't use any 3rd party tools for this, since I am transforming the data in memory.
My current method is to create a parameterized SqlCommand:
INSERT INTO #table (col1, col2, ... col200) VALUES (#col1, #col2, ... #col200)
and then for each row, clear and set the parameters and execute.
There has to be a more efficient way. I'm able to read and write the records on disk in a matter of seconds...
SqlBulkCopy will get the data in very fast.
I blogged not that long ago how to maximise performance. Some stats and examples in there. I compared 2 techniques, 1 using an SqlDataAdapter and 1 using SqlBulkCopy - bottom line was for bulk inserting 100K records, the data adapter approach took ~25 seconds compared to only ~0.8s for SqlBulkCopy.
You should use the SqlBulkCopy class.