Postgresql – Importing CSV into multiple tables using PostgreSQL

csvpostgresql

I have a very big CSV file with the following format:

TAG,TIME,VALUE

as an example row:

footag,2014-06-25 08:00:00.0,3400.0

I used to import it easily inside PostgreSQL 9.3 using the following:
COPY datapoints FROM '/home/foo/my.csv' DELIMITER ',' CSV; where datapoints is a table with the three corresponding columns.

Now, the database into which I need to import has changed. It has two tables. Here are \d+ outputs:

                                          Table "public.tags"
 Column |       Type        |                     Modifiers                     | Storage  | Stats target | Description
--------+-------------------+---------------------------------------------------+----------+--------------+-------------
 id     | integer           | not null default nextval('tags_id_seq'::regclass) | plain    |              |
 tag    | character varying | not null                                          | extended |              |
Indexes:
    "tags_pkey" PRIMARY KEY, btree (id)
    "tags_tag_idx" btree (tag)
Referenced by:
    TABLE "tag_values" CONSTRAINT "tag_values_tag_fkey" FOREIGN KEY (tag_id) REFERENCES tags(id)
Has OIDs: no

                                Table "public.tag_values"
 Column |            Type             | Modifiers | Storage | Stats target | Description
--------+-----------------------------+-----------+---------+--------------+-------------
 tag_id | integer                     | not null  | plain   |              |
 time   | timestamp without time zone | not null  | plain   |              |
 value  | double precision            |           | plain   |              |
Indexes:
    "tag_values_tag_time_idx" btree (tag_id, "time")
Foreign-key constraints:
    "tag_values_tag_fkey" FOREIGN KEY (tag_id) REFERENCES tags(id)
Has OIDs: no

So essentially, the original table is split into two. As you see, there's a foreign key constraint now (between tags(id) and tag_valued(tag_id)) Also, tags(tag) are supposed to be distinct. What is the easiest way to ingest the same .CSV files into such structure?

Thanks!

Best Answer

I find the most reliable way to import CSV data into PostgreSQL is to create a table just for the import (you can delete it after). Often there are wonky values in some columns, so I make all the column types text and then convert them on insert into the "real" table.

The \copy command in psql (different than copy) makes it easy to copy files to a remote server, and insert into ... select makes it easy to copy to another table.

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Postgresql – Postgres multiple joins slow query, how to store default child record

You write:

Each customer can have multiple sites, but only one should be displayed in this list.

Yet, your query retrieves all rows. That would be a point to optimize. But you also do not define which site is to be picked.

Either way, it does not matter much here. Your EXPLAIN shows only 5026 rows for the site scan (5018 for the customer scan). So hardly any customer actually has more than one site. Did you ANALYZE your tables before running EXPLAIN?

From the numbers I see in your EXPLAIN, indexes will give you nothing for this query. Sequential table scans will be the fastest possible way. Half a second is rather slow for 5000 rows, though. Maybe your database needs some general performance tuning?

Maybe the query itself is faster, but "half a second" includes network transfer? EXPLAIN ANALYZE would tell us more.

If this query is your bottleneck, I would suggest you implement a materialized view.

After you provided more information I find that my diagnosis pretty much holds.

The query itself needs 27 ms. Not much of a problem there. "Half a second" was the kind of misunderstanding I had suspected. The slow part is the network transfer (plus ssh encoding / decoding, possibly rendering). You should only retrieve 100 rows, that would solve most of it, even if it means to execute the whole query every time.

If you go the route with a materialized view like I proposed you could add a serial number without gaps to the table plus index on it - by adding a column row_number() OVER (<your sort citeria here>) AS mv_id.

Then you can query:

SELECT *
FROM   materialized_view
WHERE  mv_id >= 2700
AND    mv_id <  2800;

This will perform very fast. LIMIT / OFFSET cannot compete, that needs to compute the whole table before it can sort and pick 100 rows.

pgAdmin timing

When you execute a query from the query tool, the message pane shows something like:

Total query runtime: 62 ms.

And the status line shows the same time. I quote pgAdmin help about that:

The status line will show how long the last query took to complete. If a dataset was returned, not only the elapsed time for server execution is displayed, but also the time to retrieve the data from the server to the Data Output page.

If you want to see the time on the server you need to use SQL EXPLAIN ANALYZE or the built in Shift + F7keyboard shortcut or Query -> Explain analyze. Then, at the bottom of the explain output you get something like this:

Total runtime: 0.269 ms

Postgresql – Improve performance on concurrent UPDATEs for a timestamp column in Postgres

Would a different kind of column be faster? For example an integer

No. timestamp and timestamptz are just unsigned 64-bit integers internally anyway.

Is there some way to not lock the column?

It doesn't lock the column. It takes weak table lock that doesn't really block anything except DDL, and takes a row level lock on the row you're updating.

There is no way to prevent the row level lock. It exists because without it behaviour and ordering concurrent updates would be undefined. We don't like undefined behaviour in RDBMSs.

It only blocks concurrent updates of the same row anyway.

Any other tips to improve this?

Not with the detail provided. There's likely a better way to do what you're trying to do, but it'll probably involve taking a few steps back and looking for a different strategy for solving the underlying problem.

In the specific case of cache invalidation I think you might want to look into LISTEN and NOTIFY. Again though, there just isn't enough info here to go on.

Best Answer

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