Basic API

To begin your querying session, get a connection to the EvaDB using connect:

connect([evadb_dir, sql_backend])

Connects to the EvaDB server and returns a connection object.

from evadb import connect
conn = connect()

You can then use this connection to run queries:

conn.load("online_video.mp4", "youtube_video", "video").df()
conn.query("CREATE TABLE IF NOT EXISTS youtube_video_text AS SELECT SpeechRecognizer(audio) FROM youtube_video;").df()

Warning

It is important to call df to run the actual query.

EvaDB uses a lazy query execution technique to improve performance. Calling conn.query("...") will only construct and not run the query. Calling conn.query("...").df() will both construct and run the query.

EvaDBConnection Interface

cursor()

Retrieves a cursor associated with the connection.

EvaDBCursor Interface

connect([evadb_dir, sql_backend])	Connects to the EvaDB server and returns a connection object.
load(file_regex, table_name, format, **kwargs)	Loads data from files into a table.
query(sql_query)	Executes a SQL query.
table(table_name[, chunk_size, chunk_overlap])	Retrieves data from a table in the database.
create_udf(udf_name[, if_not_exists, ...])	Create a udf in the database.
create_vector_index(index_name, table_name, ...)	Creates a vector index using the provided expr on the table.
df()	Returns the result as a pandas DataFrame.
drop_table(table_name[, if_exists])	Drop a table in the database.
drop_udf(udf_name[, if_exists])	Drop a udf in the database.
drop_index(index_name[, if_exists])	Drop an index in the database.

EvaDBQuery Interface

select(expr)	Projects a set of expressions and returns a new EvaDBQuery.
cross_apply(expr, alias)	Execute a expr on all the rows of the relation
filter(expr)	Filters rows using the given condition.
df()	Execute and fetch all rows as a pandas DataFrame
alias(alias)	Returns a new Relation with an alias set.
limit(num)	Limits the result count to the number specified.
order(order_expr)	Reorder the relation based on the order_expr
show()	Execute and fetch all rows as a pandas DataFrame
sql_query()	Get the SQL query that is equivalent to the relation
execute()	Transform the relation into a result set