Python Client Examples#

This page shows how to perform common operations with the Deephaven Python Client.

Initialize#

The Session class is your connection to Deephaven. This is what allows your Python code to interact with a Deephaven server:

from pydeephaven import Session
session = Session()

Binding to a table#

The Session class has many methods that create tables. This example creates a ticking time table and binds it to Deephaven:

from pydeephaven import Session
session = Session()
table = session.time_table(period=1000000000).update(formulas=["Col1 = i % 2"])
session.bind_table(name="my_table", table=table)

This is the general flow of how the Python client interacts with Deephaven. You create a table (new or existing), execute some operations on it, and then bind it to Deephaven. Binding the table gives it a named reference on the Deephaven server, so that it can be used from the Web API or other Sessions.

Execute a query on a table#

table.update() can be used to execute an update on a table. This updates a table with a query string:

from pydeephaven import Session
session = Session()

# Create a table with no columns and 3 rows
table = session.empty_table(3)

# Create derived table having a new column MyColumn populated with the row index "i"
table = table.update(["MyColumn = i"])

# Update the Deephaven Web Console with this new table
session.bind_table(name="my_table", table=table)

Sort a table#

table.sort() can be used to sort a table. This example sorts a table by one of its columns:

from pydeephaven import Session
session = Session()
table = session.empty_table(5)
table = table.update(["SortColumn = 4-i"])
table = table.sort(["SortColumn"])
session.bind_table(name="my_table", table=table)

Filter a table#

table.where() can be used to filter a table. This example filters a table using a filter string:

from pydeephaven import Session
session = Session()
table = session.empty_table(5)
table = table.update(["Values = i"])
table = table.where(["Values % 2 == 1"])
session.bind_table(name="my_table", table=table)

Query objects#

Query objects are a way to create and manage a sequence of Deephaven query operations as a single unit. Query objects have the potential to perform better than the corresponding individual queries, because the Query object can be transmitted to the server in one request rather than several, and because the system can perform certain optimizations when it is able to see the whole sequence of queries at once. They are similar in spirit to prepared statements in SQL.

The general flow of using a Query object is to construct a query with a table, call the table operations (sort, where, update, etc.) on the Query object, and then assign your table to the return value of query.exec().

Any operation that can be executed on a table can also be executed on a Query object. This example shows two operations that compute the same result, with the first one using the table updates and the second one using a Query object:

from pydeephaven import Session
session = Session()
table = session.empty_table(10)

# executed immediately
table1= table.update(["MyColumn = i"]).sort(["MyColumn"]).where(["MyColumn > 5"]);

# create Query Object (execution is deferred until the "exec" statement)
query_obj = session.query(table)
    .update(["MyColumn = i"])
    .sort(["MyColumn"])
    .where(["MyColumn > 5"]);

# Transmit the QueryObject to the server and execute it
table2 = query_obj.exec();

session.bind_table(name="my_table1", table=table1)
session.bind_table(name="my_table2", table=table2)

Join 2 tables#

table.join() is one of many operations that can join two tables, as shown below:

from pydeephaven import Session
session = Session()
table1 = session.empty_table(5)
table1 = table1.update(["Values1 = i", "Group = i"])
table2 = session.empty_table(5)
table2 = table2.update(["Values2 = i + 10", "Group = i"])
table = table1.join(table2, on=["Group"])
session.bind_table(name="my_table", table=table)

Perform aggregations on a table#

Aggregations can be applied on tables in the Python client. This example creates a aggregation that averages the Count column of a table, and aggregates it by the Group column:

from pydeephaven import Session, agg
session = Session()
table = session.empty_table(10)
table = table.update(["Count = i", "Group = i % 2"])
my_agg = agg.avg(["Count"])
table = table.agg_by(aggs=[my_agg], by=["Group"])
session.bind_table(name="my_table", table=table)

Convert a PyArrow table to a Deephaven table#

Deephaven natively supports PyArrow tables. This example converts between a PyArrow table and a Deephaven table:

import pyarrow as pa
from pydeephaven import Session
session = Session()
arr = pa.array([4,5,6], type=pa.int32())
pa_table = pa.Table.from_arrays([arr], names=["Integers"])
table = session.import_table(pa_table)
session.bind_table(name="my_table", table=table)
#Convert the Deephaven table back to a pyarrow table
pa_table = table.to_arrow()

Execute a script server side#

session.run_script() can be used to execute code on the Deephaven server. This is useful when operations cannot be done on the client-side, such as creating a dynamic table writer. This example shows how to execute a script server-side and retrieve a table generated from the script:

from pydeephaven import Session
session = Session()

script = """
from deephaven import empty_table
table = empty_table(8).update(["Index = i"])
"""

session.run_script(script)
table = session.open_table("table")
print(table.to_arrow())

Subscribe to a ticking table#

The pydeephaven-ticking package can be used to subscribe to ticking tables. This is useful for getting asynchronous callbacks when they change. The package maintains a complete local copy of the table and notifies callers when the table changes.

Note that pydeephaven-ticking must be built before running this example. Build instructions are available `here https://github.com/deephaven/deephaven-core/tree/main/py/client-ticking#readme`_.

The listener can be specified either as a python function or as an implementation of the TableListener abstract base class. In the case of implementing TableListener TableListener, the caller needs to implement on_update and optionally on_error

as shown in the example:

import time
from pydeephaven import Session, TableListener, TableUpdate, listen

session = Session()
table = session.time_table(period=1000000000).update(formulas=["Col1 = i % 2"])

class MyListener(TableListener):
    def on_update(self, update: TableUpdate) -> None:
        self._show_deltas("removes", update.removed())
        self._show_deltas("adds", update.added())
        self._show_deltas("modified-prev", update.modified_prev())
        self._show_deltas("modified", update.modified())

    def on_error(self, error: Exception):
        print(f"Error happened: {error}")

    def _show_deltas(self, what: str, dict: Dict[str, pa.Array]):
        if len(dict) == 0:
            return

        print(f"*** {what} ***")
        for name, data in dict.items():
            print(f"name={name}, data={data}")

listen_handle = listen(table, MyListener())
# Start processing data in another thread
listen_handle.start()
time.sleep(15)  # simulate doing other work for 15 seconds
listen_handle.stop()

The on_update callback method is invoked with a TableUpdate argument. TableUpdate argument. TableUpdate has methods added(), removed(), modified_prev(), and modified(). These methods return the data that was added, removed, or modified in this update. modified_prev() returns the data as it was before the modify operation happened, whereas modified() returns the modified data. This can be useful e.g. for calculations like keeping a running sum, where it is useful to know the “old” value and the new value.

Each of the above methods has a “chunked” variant that returns a generator. This may be useful if the client is processing so much data that it would like to handle it a chunk at a time. The chunked variants are added_chunks(), removed_chunks(), modified_prev_chunks(), and modified_chunks().

Error handling#

A DHError is thrown whenever the client package encounters an error. This example shows how to catch a DHError:

from pydeephaven import Session, DHError
try:
    session = Session(host="invalid_host")
except DHError as e:
    print("Deephaven error when connecting to session")
    print(e)
except Exception as e:
    print("Unknown error")
    print(e)