I want to extract information from a text file (almost 1GB) and store it in PostgreSQL database.
Text file is in following format:
DEBUG, 2017-03-23T10:02:27+00:00, ghtorrent-40 -- ghtorrent.rb:Repo EFForg/https-everywhere exists
DEBUG, 2017-03-24T12:06:23+00:00, ghtorrent-49 -- ghtorrent.rb:Repo Shikanime/print exists
...
and I want to extract 'DEBUG', timestamp, 'ghtorrent-40', 'ghtorrent' and "Repo EFForg/https-everywhere exists" from each line and store it in database.
I have done it in using other languages like python (psycopg2) and C++ (libpqxx) but is it possible to write a function in PostgreSQL itself to import the whole data itself.
I am currenly using pgAdmin4 tool for the PostgreSQL.
I thinking of using something like pg_read_file in function to read the file but one line at a time and insert it into the table.
An approach I use with my large XML files - 130GB or bigger - is to upload the whole file into a temporary unlogged table and from there I extract the content I want. Unlogged tables are not crash-safe, but are much faster than logged ones, which totally suits the purpose of a temporary table ;-)
Considering the following table ..
CREATE UNLOGGED TABLE tmp (raw TEXT);
.. you can import this 1GB file using a single psql line from your console (unix)..
$ cat 1gb_file.txt | psql -d db -c "COPY tmp FROM STDIN"
After that all you need is to apply your logic to query and extract the information you want. Depending on the size of your table, you can create a second table from a SELECT, e.g.:
CREATE TABLE t AS
SELECT
trim((string_to_array(raw,','))[1]) AS operation,
trim((string_to_array(raw,','))[2])::timestamp AS tmst,
trim((string_to_array(raw,','))[3]) AS txt
FROM tmp
WHERE raw LIKE '%DEBUG%' AND
raw LIKE '%ghtorrent-40%' AND
raw LIKE '%Repo EFForg/https-everywhere exists%'
Adjust the string_to_array function and the WHERE clause to your logic! Optionally you can replace these multiple LIKE operations to a single SIMILAR TO.
.. and your data would be ready to be played with:
SELECT * FROM t;
operation | tmst | txt
-----------+---------------------+------------------------------------------------------------------
DEBUG | 2017-03-23 10:02:27 | ghtorrent-40 -- ghtorrent.rb:Repo EFForg/https-everywhere exists
(1 Zeile)
Once your data is extracted you can DROP TABLE tmp; to free some disk space ;)
Further reading: COPY, PostgreSQL array functions and pattern matching
UPD: Where from the requirement is coming.
My friend is using Mnemosine (http://mnemosyne-proj.org/) which is python program that uses sqlite as db. The issue that mobile version works only with one database file and my friend has already several. So he asked me if I can merge two databases.
So! I have two sqlite db files with same schema but different data.
Is there an automated way to include data from one file to another? I just need to insert additional values to dictionary tables and correctly insert values from other tables based on new ids.
Unfortunately there are no foreign keys defined so I need probably first specify columns/tables relationship. But in general, if I solve relationship issue, is it possible to merge dbs?
You can open the database you want to merge into, then attach the other database.
ATTACH DATABASE "foo.database" AS foo;
Then you can access the other database's tables by prefixing it with the database's name and a dot:
INSERT INTO bar (baz) SELECT baz FROM foo.bar;
You could try this:
sqlite3 bar.db ".dump t1" | grep -v "^CREATE" | sqlite3 foo.db
That will put the contents of table t1 from bar.db into table t1 in foo.db.
I have a little problem. My friend has a database with over 10 tables and each table has over 90-100 records.
I can't find a workaround to export the records (to put in a SQL file something like this: INSERT INTO .... VALUES ... for each existing records) from his tables to import in my database.
How to do that ?
I tried: right click on a table -> Script Table as -> INSERT TO -> File ...
but it only generate the INSERT statement.
There are a solution ? or this feature is only for commercial version ?
UPDATE
You can use BCP command with command prompt like this
For export: bcp ADatabase.dbo.OneTable out d:\test\OneTable.bcp -c -Usa -Ppassword
For import: bcp ADatabase.dbo.OneTable in d:\test\OneTable.bcp -c -Usa -Ppassword
these commands will create a BCP file which contains records for specified table. You can import using existing BCP file into another database
If you use remote database then:
bcp ADatabaseRemote.dbo.OneTableRemote out d:\test\OneTableRemote.bcp -Slocalhost/SQLExpress -Usa -Ppassword
Instead of localhost/SQLExpress, you can use localhost or other server name...
Probably the simplest way to do this would be to run a SELECT statement that outputs to a file. Then you can import that data into your database.
For simple moves, I have also done a copy/paste manually. Sometimes it is better to use Excel as a staging platform before pasting it into the new database. You may need to create a temporary table in your new database that matches up exactly with the data you are pasting over. For example, I usually don't put a PK on the temp table at first and make the PK field just an INT. That way the copy will go smoother.
In the corporate world, you would use SSIS to move this data around.
a couple of ways you could do this. One,select everything from each table and save the results as a csv or delimited file (you can do this from sql management studio). You can also script the tables as create and copy the scripts over to the new database, assuming it is a sql server also. Then for import use the load infile statement. You may have to google the syntax for sql server but I know this works in mysql and oracle. haven't tried it in sql server yet.
LOAD DATA INFILE 'myfile'
INTO TABLE stuff
FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'
SET id = NULL;
Or if you are going to another sql server use the sql export import wizard.
http://msdn.microsoft.com/en-us/library/ms141209.aspx
I wish to use an external Text/CSV file to read data and run an SQL Query. Is this possible without using the External_Table concept? I do not have write permissions in the DB, hence cannot create a temp table in the DB.
Basically, I have a list of employee numbers (around 100) in a text file, using which I wish to run the following query each time:
SELECT emp_record FROM emp_data WHERE emp_no = "#file-containing-number"
I have to run a series of tasks on these and they are in no particular order or sequence, but have been provided in that text file as a list.
I am using the TOAD client and have only read-only permissions on the DB I connect to.
When I do this sort of thing I will open the file in notepad, add a comma to the end of each line and use the following SQL query:
select emp_record FROM emp_data WHERE emp_no IN (
... Paste contents of file here.
)
No - based on the limitations you mention in your question.
Are you saying you cannot even insert these records into a table in the database? Who is imposing these restrictions? You have a job to do. Other support staff should help in providing a means to accomplish the job.
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);