I found that tensorflow has a way to handle sqlite queries [see here]. However, how would one proceed if I need to attached several databases to the main database before I can perform the query? Is there still a way to use this particular sqlite queries support in tensorflow?
Hi @edamondo. Can you please tell what you mean about “attach[ing] several databases”? I’m not sure I understand what you mean,
c.execute('ATTACH DATABASE "db_1.sqlite" AS db_1')
attaches a database to the one that was opened with
conn = sqlite3.connect('db_master.sqlite')
So I could join tables from the two different databases and perform queries on the joined tables.
Is it more clear now?
After trying out a few things on my side, it looks like on can combine different SQL
statements under the unique argument
tf.data.experimental.SqlDataset. You just have to separate them by the usual semi-column.
Something along this line:
dataset = tf.data.experimental.SqlDataset(driver_name='sqlite', data_source_name= '/foo/bar.sqlite3', query='ATTACH DATABASE "foo/otherDB.sqlite3" AS AttachedDB;\ SELECT name, age FROM AttachedDB.tableInAttachedDB', (tf.string, tf.int32))
@tagoma, thank you this helped me. I still couldn’t query my databasis since the records contains tensors arrays (like tf.constant([10, 20,30])) or numpy arrays (like np.array([50,60,70])) and it seems that tf.Dtype in output_type [see here] doesn’t support such array types. Or am I making a mistake?
I’m not sure I understand you set up.
What do you mean “records contains tensors arrays”, please?