One might need to insert a large amount of data when first populating a database. This section contains some suggestions on how to make this process as efficient as possible.
    When using multiple INSERTs, turn off autocommit and just do
    one commit at the end.  (In plain
    SQL, this means issuing BEGIN at the start and
    COMMIT at the end.  Some client libraries might
    do this behind your back, in which case you need to make sure the
    library does it when you want it done.)  If you allow each
    insertion to be committed separately,
    PostgreSQL is doing a lot of work for
    each row that is added.  An additional benefit of doing all
    insertions in one transaction is that if the insertion of one row
    were to fail then the insertion of all rows inserted up to that
    point would be rolled back, so you won't be stuck with partially
    loaded data.
   
COPY
    Use COPY to load
    all the rows in one command, instead of using a series of
    INSERT commands.  The COPY
    command is optimized for loading large numbers of rows; it is less
    flexible than INSERT, but incurs significantly
    less overhead for large data loads. Since COPY
    is a single command, there is no need to disable autocommit if you
    use this method to populate a table.
   
    If you cannot use COPY, it might help to use PREPARE to create a
    prepared INSERT statement, and then use
    EXECUTE as many times as required.  This avoids
    some of the overhead of repeatedly parsing and planning
    INSERT. Different interfaces provide this facility
    in different ways; look for “prepared statements” in the interface
    documentation.
   
    Note that loading a large number of rows using
    COPY is almost always faster than using
    INSERT, even if PREPARE is used and
    multiple insertions are batched into a single transaction.
   
    COPY is fastest when used within the same
    transaction as an earlier CREATE TABLE or
    TRUNCATE command. In such cases no WAL
    needs to be written, because in case of an error, the files
    containing the newly loaded data will be removed anyway.
    However, this consideration only applies when
    wal_level is minimal for
    non-partitioned tables as all commands must write WAL otherwise.
   
    If you are loading a freshly created table, the fastest method is to
    create the table, bulk load the table's data using
    COPY, then create any indexes needed for the
    table.  Creating an index on pre-existing data is quicker than
    updating it incrementally as each row is loaded.
   
If you are adding large amounts of data to an existing table, it might be a win to drop the indexes, load the table, and then recreate the indexes. Of course, the database performance for other users might suffer during the time the indexes are missing. One should also think twice before dropping a unique index, since the error checking afforded by the unique constraint will be lost while the index is missing.
Just as with indexes, a foreign key constraint can be checked “in bulk” more efficiently than row-by-row. So it might be useful to drop foreign key constraints, load data, and re-create the constraints. Again, there is a trade-off between data load speed and loss of error checking while the constraint is missing.
What's more, when you load data into a table with existing foreign key constraints, each new row requires an entry in the server's list of pending trigger events (since it is the firing of a trigger that checks the row's foreign key constraint). Loading many millions of rows can cause the trigger event queue to overflow available memory, leading to intolerable swapping or even outright failure of the command. Therefore it may be necessary, not just desirable, to drop and re-apply foreign keys when loading large amounts of data. If temporarily removing the constraint isn't acceptable, the only other recourse may be to split up the load operation into smaller transactions.
maintenance_work_mem
    Temporarily increasing the maintenance_work_mem
    configuration variable when loading large amounts of data can
    lead to improved performance.  This will help to speed up CREATE
    INDEX commands and ALTER TABLE ADD FOREIGN KEY commands.
    It won't do much for COPY itself, so this advice is
    only useful when you are using one or both of the above techniques.
   
max_wal_size
    Temporarily increasing the max_wal_size
    configuration variable can also
    make large data loads faster.  This is because loading a large
    amount of data into PostgreSQL will
    cause checkpoints to occur more often than the normal checkpoint
    frequency (specified by the checkpoint_timeout
    configuration variable). Whenever a checkpoint occurs, all dirty
    pages must be flushed to disk. By increasing
    max_wal_size temporarily during bulk
    data loads, the number of checkpoints that are required can be
    reduced.
   
    When loading large amounts of data into an installation that uses
    WAL archiving or streaming replication, it might be faster to take a
    new base backup after the load has completed than to process a large
    amount of incremental WAL data.  To prevent incremental WAL logging
    while loading, disable archiving and streaming replication, by setting
    wal_level to minimal,
    archive_mode to off, and
    max_wal_senders to zero.
    But note that changing these settings requires a server restart.
   
    Aside from avoiding the time for the archiver or WAL sender to
    process the WAL data,
    doing this will actually make certain commands faster, because they
    are designed not to write WAL at all if wal_level
    is minimal.  (They can guarantee crash safety more cheaply
    by doing an fsync at the end than by writing WAL.)
    This applies to the following commands:
    
       CREATE TABLE AS SELECT
      
       CREATE INDEX (and variants such as
       ALTER TABLE ADD PRIMARY KEY)
      
       ALTER TABLE SET TABLESPACE
      
       CLUSTER
      
       COPY FROM, when the target table has been
       created or truncated earlier in the same transaction
      
ANALYZE Afterwards
    Whenever you have significantly altered the distribution of data
    within a table, running ANALYZE is strongly recommended. This
    includes bulk loading large amounts of data into the table.  Running
    ANALYZE (or VACUUM ANALYZE)
    ensures that the planner has up-to-date statistics about the
    table.  With no statistics or obsolete statistics, the planner might
    make poor decisions during query planning, leading to poor
    performance on any tables with inaccurate or nonexistent
    statistics.  Note that if the autovacuum daemon is enabled, it might
    run ANALYZE automatically; see
    Section 24.1.3
    and Section 24.1.6 for more information.
   
Dump scripts generated by pg_dump automatically apply several, but not all, of the above guidelines. To reload a pg_dump dump as quickly as possible, you need to do a few extra things manually. (Note that these points apply while restoring a dump, not while creating it. The same points apply whether loading a text dump with psql or using pg_restore to load from a pg_dump archive file.)
    By default, pg_dump uses COPY, and when
    it is generating a complete schema-and-data dump, it is careful to
    load data before creating indexes and foreign keys.  So in this case
    several guidelines are handled automatically.  What is left
    for you to do is to:
    
       Set appropriate (i.e., larger than normal) values for
       maintenance_work_mem and
       max_wal_size.
      
       If using WAL archiving or streaming replication, consider disabling
       them during the restore. To do that, set archive_mode
       to off,
       wal_level to minimal, and
       max_wal_senders to zero before loading the dump.
       Afterwards, set them back to the right values and take a fresh
       base backup.
      
       Experiment with the parallel dump and restore modes of both
       pg_dump and pg_restore and find the
       optimal number of concurrent jobs to use. Dumping and restoring in
       parallel by means of the -j option should give you a
       significantly higher performance over the serial mode.
      
       Consider whether the whole dump should be restored as a single
       transaction.  To do that, pass the -1 or
       --single-transaction command-line option to
       psql or pg_restore. When using this
       mode, even the smallest of errors will rollback the entire restore,
       possibly discarding many hours of processing.  Depending on how
       interrelated the data is, that might seem preferable to manual cleanup,
       or not.  COPY commands will run fastest if you use a single
       transaction and have WAL archiving turned off.
      
       If multiple CPUs are available in the database server, consider using
       pg_restore's --jobs option.  This
       allows concurrent data loading and index creation.
      
       Run ANALYZE afterwards.
      
    A data-only dump will still use COPY, but it does not
    drop or recreate indexes, and it does not normally touch foreign
    keys.
     [13]
    So when loading a data-only dump, it is up to you to drop and recreate
    indexes and foreign keys if you wish to use those techniques.
    It's still useful to increase max_wal_size
    while loading the data, but don't bother increasing
    maintenance_work_mem; rather, you'd do that while
    manually recreating indexes and foreign keys afterwards.
    And don't forget to ANALYZE when you're done; see
    Section 24.1.3
    and Section 24.1.6 for more information.
   
[13] 
       You can get the effect of disabling foreign keys by using
       the --disable-triggers option — but realize that
       that eliminates, rather than just postpones, foreign key
       validation, and so it is possible to insert bad data if you use it.