Add database/table create/delete function for Timestream.

awswrangler/timestream.py

@@ -234,3 +234,199 @@ def query(sql: str, boto3_session: Optional[boto3.Session] = None) -> pd.DataFra
         if col["type"] == "VARCHAR":
             df[col["name"]] = df[col["name"]].astype("string")
     return df
+
+
+def create_database(
+    database: str,
+    kms_key_id: Optional[str] = None,
+    tags: Optional[Dict[str, str]] = None,
+    boto3_session: Optional[boto3.Session] = None,
+) -> str:
+    """Create a new Timestream database.
+
+    Note
+    ----
+    If the KMS key is not specified, the database will be encrypted with a
+    Timestream managed KMS key located in your account.
+
+    Parameters
+    ----------
+    database: str
+        Database name.
+    kms_key_id: Optional[str]
+        The KMS key for the database. If the KMS key is not specified,
+        the database will be encrypted with a Timestream managed KMS key located in your account.
+    tags: Optional[Dict[str, str]]
+        Key/Value dict to put on the database.
+        Tags enable you to categorize databases and/or tables, for example,
+        by purpose, owner, or environment.
+        e.g. {"foo": "boo", "bar": "xoo"})
+    boto3_session : boto3.Session(), optional
+        Boto3 Session. The default boto3 Session will be used if boto3_session receive None.
+
+    Returns
+    -------
+    str
+        The Amazon Resource Name that uniquely identifies this database. (ARN)
+
+    Examples
+    --------
+    Creating a database.
+
+    >>> import awswrangler as wr
+    >>> arn = wr.timestream.create_database("MyDatabase")
+
+    """
+    client: boto3.client = _utils.client(service_name="timestream-write", session=boto3_session)
+    args: Dict[str, Any] = {"DatabaseName": database}
+    if kms_key_id is not None:
+        args["KmsKeyId"] = kms_key_id
+    if tags is not None:
+        args["Tags"] = [{"Key": k, "Value": v} for k, v in tags.items()]
+    response: Dict[str, Dict[str, Any]] = client.create_database(**args)
+    return cast(str, response["Database"]["Arn"])
+
+
+def delete_database(
+    database: str,
+    boto3_session: Optional[boto3.Session] = None,
+) -> None:
+    """Delete a given Timestream database. This is an irreversible operation.
+
+    After a database is deleted, the time series data from its tables cannot be recovered.
+
+    All tables in the database must be deleted first, or a ValidationException error will be thrown.
+
+    Due to the nature of distributed retries,
+    the operation can return either success or a ResourceNotFoundException.
+    Clients should consider them equivalent.
+
+    Parameters
+    ----------
+    database: str
+        Database name.
+    boto3_session : boto3.Session(), optional
+        Boto3 Session. The default boto3 Session will be used if boto3_session receive None.
+
+    Returns
+    -------
+    None
+        None.
+
+    Examples
+    --------
+    Deleting a database
+
+    >>> import awswrangler as wr
+    >>> arn = wr.timestream.delete_database("MyDatabase")
+
+    """
+    client: boto3.client = _utils.client(service_name="timestream-write", session=boto3_session)
+    client.delete_database(DatabaseName=database)
+
+
+def create_table(
+    database: str,
+    table: str,
+    memory_retention_hours: int,
+    magnetic_retention_days: int,
+    tags: Optional[Dict[str, str]] = None,
+    boto3_session: Optional[boto3.Session] = None,
+) -> str:
+    """Create a new Timestream database.
+
+    Note
+    ----
+    If the KMS key is not specified, the database will be encrypted with a
+    Timestream managed KMS key located in your account.
+
+    Parameters
+    ----------
+    database: str
+        Database name.
+    table: str
+        Table name.
+    memory_retention_hours: int
+        The duration for which data must be stored in the memory store.
+    magnetic_retention_days: int
+        The duration for which data must be stored in the magnetic store.
+    tags: Optional[Dict[str, str]]
+        Key/Value dict to put on the table.
+        Tags enable you to categorize databases and/or tables, for example,
+        by purpose, owner, or environment.
+        e.g. {"foo": "boo", "bar": "xoo"})
+    boto3_session : boto3.Session(), optional
+        Boto3 Session. The default boto3 Session will be used if boto3_session receive None.
+
+    Returns
+    -------
+    str
+        The Amazon Resource Name that uniquely identifies this database. (ARN)
+
+    Examples
+    --------
+    Creating a table.
+
+    >>> import awswrangler as wr
+    >>> arn = wr.timestream.create_table(
+    ...     database="MyDatabase",
+    ...     table="MyTable",
+    ...     memory_retention_hours=3,
+    ...     magnetic_retention_days=7
+    ... )
+
+    """
+    client: boto3.client = _utils.client(service_name="timestream-write", session=boto3_session)
+    args: Dict[str, Any] = {
+        "DatabaseName": database,
+        "TableName": table,
+        "RetentionProperties": {
+            "MemoryStoreRetentionPeriodInHours": memory_retention_hours,
+            "MagneticStoreRetentionPeriodInDays": magnetic_retention_days,
+        },
+    }
+    if tags is not None:
+        args["Tags"] = [{"Key": k, "Value": v} for k, v in tags.items()]
+    response: Dict[str, Dict[str, Any]] = client.create_table(**args)
+    return cast(str, response["Table"]["Arn"])
+
+
+def delete_table(
+    database: str,
+    table: str,
+    boto3_session: Optional[boto3.Session] = None,
+) -> None:
+    """Delete a given Timestream table.
+
+    This is an irreversible operation.
+
+    After a Timestream database table is deleted, the time series data stored in the table cannot be recovered.
+
+    Due to the nature of distributed retries,
+    the operation can return either success or a ResourceNotFoundException.
+    Clients should consider them equivalent.
+
+    Parameters
+    ----------
+    database: str
+        Database name.
+    table: str
+        Table name.
+    boto3_session : boto3.Session(), optional
+        Boto3 Session. The default boto3 Session will be used if boto3_session receive None.
+
+    Returns
+    -------
+    None
+        None.
+
+    Examples
+    --------
+    Deleting a table
+
+    >>> import awswrangler as wr
+    >>> arn = wr.timestream.delete_table("MyDatabase", "MyTable")
+
+    """
+    client: boto3.client = _utils.client(service_name="timestream-write", session=boto3_session)
+    client.delete_table(DatabaseName=database, TableName=table)

docs/source/api.rst

@@ -159,8 +159,12 @@ Amazon Timestream
 .. autosummary::
     :toctree: stubs
 
-    write
+    create_database
+    create_table
+    delete_database
+    delete_table
     query
+    write
 
 Amazon EMR
 ----------

tests/conftest.py

@@ -231,3 +231,14 @@ def mysql_table():
     with con.cursor() as cursor:
         cursor.execute(f"DROP TABLE IF EXISTS test.{name}")
     con.close()
+
+
+@pytest.fixture(scope="function")
+def timestream_database_and_table():
+    name = f"tbl_{get_time_str_with_random_suffix()}"
+    print(f"Timestream name: {name}")
+    wr.timestream.create_database(name)
+    wr.timestream.create_table(name, name, 1, 1)
+    yield name
+    wr.timestream.delete_table(name, name)
+    wr.timestream.delete_database(name)

tests/test_timestream.py

@@ -8,7 +8,8 @@
 logging.getLogger("awswrangler").setLevel(logging.DEBUG)
 
 
-def test_write_simple():
+def test_basic_scenario(timestream_database_and_table):
+    name = timestream_database_and_table
     df = pd.DataFrame(
         {
             "time": [datetime.now(), datetime.now(), datetime.now()],
@@ -19,16 +20,34 @@ def test_write_simple():
     )
     rejected_records = wr.timestream.write(
         df=df,
-        database="sampleDB",
-        table="sampleTable",
+        database=name,
+        table=name,
         time_col="time",
         measure_col="measure",
         dimensions_cols=["dim0", "dim1"],
     )
     assert len(rejected_records) == 0
+    df = wr.timestream.query(
+        f"""
+        SELECT
+            1 as col_int,
+            try_cast(now() as time) as col_time,
+            TRUE as col_bool,
+            current_date as col_date,
+            'foo' as col_str,
+            measure_value::double,
+            measure_name,
+            time
+        FROM "{name}"."{name}"
+        ORDER BY time
+        DESC LIMIT 10
+    """
+    )
+    assert df.shape == (3, 8)
 
 
-def test_write_real():
+def test_real_csv_load_scenario(timestream_database_and_table):
+    name = timestream_database_and_table
     df = pd.read_csv(
         "https://raw.githubusercontent.com/awslabs/amazon-timestream-tools/master/sample_apps/data/sample.csv",
         names=[
@@ -52,39 +71,21 @@ def test_write_real():
     df_memory = df[df.measure_kind == "memory_utilization"]
     rejected_records = wr.timestream.write(
         df=df_cpu,
-        database="sampleDB",
-        table="sampleTable",
+        database=name,
+        table=name,
         time_col="time",
         measure_col="measure",
         dimensions_cols=["index", "region", "az", "hostname"],
     )
     assert len(rejected_records) == 0
     rejected_records = wr.timestream.write(
         df=df_memory,
-        database="sampleDB",
-        table="sampleTable",
+        database=name,
+        table=name,
         time_col="time",
         measure_col="measure",
         dimensions_cols=["index", "region", "az", "hostname"],
     )
     assert len(rejected_records) == 0
-
-
-def test_query():
-    df = wr.timestream.query(
-        """
-    SELECT
-        1 as col_int,
-        try_cast(now() as time) as col_time,
-        TRUE as col_bool,
-        current_date as col_date,
-        'foo' as col_str,
-        measure_value::double,
-        measure_name,
-        time
-    FROM "sampleDB"."sampleTable"
-    ORDER BY time
-    DESC LIMIT 10
-    """
-    )
-    assert df.shape == (10, 8)
+    df = wr.timestream.query(f'SELECT COUNT(*) AS counter FROM "{name}"."{name}"')
+    assert df["counter"].iloc[0] == 126_000