Database Performance

Pele uses the python package sqlalchemy to manage the database of minima and transition states. By default this uses sqlite to store the database in a file on the hard drive. This is very convenient for persistent storage of data and for easy access. The downside is that without careful use it can be very slow. For most purposes it shouldn’t cause a problem; this information about optimizing database performance should only be useful if you’re dealing with very large databases or are accessing the database hundreds of times a second. This should be considered an expert level tutorial. Some of the possible things to watch out for are listed below.

Filter and sort on the database level

Try to minimize how many objects are retrieved from the database. Do any filtering on the SQL level using sqlalchemy query commands. For example:

minima = db.session.query(Minimum).filter( < 0).all()

is much faster than:

minima = db.session.query(Minimum) # load all the minima
minima = filter(lambda m: < 0, minima) # filter in python

Bulk inserts

If you have lots of minima you want to add to the database all at once it is much faster to do it all at once than one at a time. For example, if you have a long list of energy and coordinates you can do it the slow way:

# loop through the minima adding them one at a time
for e, coords in e_coords_list:
    mnew = Minimum(e, coords)

or the fast way using bulk inserts:

# make a list of dictionaries for the minima data
minima_dicts = []
for e, coords in e_coords_list:
    min_dict = dict(energy=e, coords=coords)
    # you can add other data (e.g. fvib) to min_dict as well
# add them to the database all at once
self.db.engine.execute(Minimum.__table__.insert(), minima_dicts)

Loading of deferred data

We have it set up so that some of the columns in the database (e.g. Minimum.coords and other large objects) are deferred, which means that they are only loaded from the database when they are actually accessed. The upside is that the, possibly substantial, coordinate array need not be transferred from hard disk to memory each time a minimum is accesses. Code such as:

for m in db.session.query(Minimum):

runs slightly faster than it otherwise would.

Deferring the coords has a significant downside, in that the database must be queried for every minimum for which you want to access the coords. The result is that the following code:

for m in db.session.query(Minimum):
    print m.coords

can be extremely slow if you are looping through a lot of minima. If there are 10000 minima it will send 10001 queries to the database. The more minima there are the slower it will be.

Luckily the solution is easy. You simply need to undefer the columns you want to access in the original query.:

from sqlalchemy.orm import undefer
for m in db.session.query(Minimum).options(undefer("coords")):
    print m.coords

This results in only one query to the database.

Frequent committing

Normally when you change the database using sqlalchemy the changes are made in memory only. The command db.session.commit() must be called to make changes to the database on the hard drive. If this is called extremely frequently (hundreds of times a second) it can add up to significant overhead. Again, the solution is easy: call commit() only after a sufficient number of changes have accrued.

Searching via an attribute that is not indexed

SQL indices mean that an SQL search happens in logarithmic time rather than linear time. If you search via an attribute that is not indexed it can be slow for large databases. For example:

db.session.query(Minimum).filter(Minimum.fvib > 100)

If you find yourself doing this often, you can manually add a filter to your database. In SQL this is:

CREATE INDEX idx_minimum_fvib ON tbl_minima (fvib);

With sqlalchemy this would be:

sqlalchemy.schema.Index('idx_minimum_fvib', Minimum.__table__.c.fvib)


As an example, the image below shows the mean time per step that is spent in the Database.addMinimum() routine during a basinhopping run. By the end of the run the database has about 20000 minima in it.


The figure shows the effect of three variables.

1. Frequent commits (every step) vs rare commits (every 100th step). This effect can be significant, but it does not grow with the size of the database.

2. Having an SQL index on the energies of the minima. To ensure we don’t have duplicate minima in the database we search for minima within an energy window. The index really helps with scaling and keeps things fast for large databases. Pele already has an energy index, so this is just for example purposes.

3. Loading coords immediately via the undefer option. If the energies are similar the coordinates are checked to test whether two minima are the same. If there are many minima with similar energy this is done multiple times. Loading the deferred coordinate arrays takes more time for larger databases where the number of minima with similar energy is greater. Pele already uses undefer in Database.addMinimum(), so this is just for example purposes.

These three effects can be important, but the actual time per call is still very small. You would need to do tens or hundreds of basinhopping steps a second for any of this to become an important factor. This will only be a problem for for simple, or small systems.