Add version param for wr.timestream.write

awswrangler/timestream.py

@@ -32,6 +32,7 @@ def _write_batch(
     table: str,
     cols_names: List[str],
     measure_type: str,
+    version: int,
     batch: List[Any],
     boto3_primitives: _utils.Boto3PrimitivesType,
 ) -> List[Dict[str, str]]:
@@ -59,6 +60,7 @@ def _write_batch(
                     "MeasureValue": str(rec[1]),
                     "Time": str(round(rec[0].timestamp() * 1_000)),
                     "TimeUnit": "MILLISECONDS",
+                    "Version": version,
                 }
                 for rec in batch
             ],
@@ -117,6 +119,7 @@ def write(
     time_col: str,
     measure_col: str,
     dimensions_cols: List[str],
+    version: int = 1,
     num_threads: int = 32,
     boto3_session: Optional[boto3.Session] = None,
 ) -> List[Dict[str, str]]:
@@ -136,6 +139,9 @@ def write(
         DataFrame column name to be used as measure.
     dimensions_cols : List[str]
         List of DataFrame column names to be used as dimensions.
+    version : int
+        Version number used for upserts.
+        Documentation https://docs.aws.amazon.com/timestream/latest/developerguide/API_WriteRecords.html.
     num_threads : str
         Number of thread to be used for concurrent writing.
     boto3_session : boto3.Session(), optional
@@ -185,6 +191,7 @@ def write(
                 itertools.repeat(table),
                 itertools.repeat(cols_names),
                 itertools.repeat(measure_type),
+                itertools.repeat(version),
                 batches,
                 itertools.repeat(_utils.boto3_to_primitives(boto3_session=boto3_session)),
             )

tests/test_timestream.py

@@ -46,6 +46,59 @@ def test_basic_scenario(timestream_database_and_table):
     assert df.shape == (3, 8)
 
 
+def test_versioned(timestream_database_and_table):
+    name = timestream_database_and_table
+    time = [datetime.now(), datetime.now(), datetime.now()]
+    dfs = [
+        pd.DataFrame(
+            {
+                "time": time,
+                "dim0": ["foo", "boo", "bar"],
+                "dim1": [1, 2, 3],
+                "measure": [1.0, 1.1, 1.2],
+            }
+        ),
+        pd.DataFrame(
+            {
+                "time": time,
+                "dim0": ["foo", "boo", "bar"],
+                "dim1": [1, 2, 3],
+                "measure": [1.0, 1.1, 1.9],
+            }
+        ),
+        pd.DataFrame(
+            {
+                "time": time,
+                "dim0": ["foo", "boo", "bar"],
+                "dim1": [1, 2, 3],
+                "measure": [1.0, 1.1, 1.9],
+            }
+        ),
+    ]
+    versions = [1, 1, 2]
+    rejected_rec_nums = [0, 1, 0]
+    for df, version, rejected_rec_num in zip(dfs, versions, rejected_rec_nums):
+        rejected_records = wr.timestream.write(
+            df=df,
+            database=name,
+            table=name,
+            time_col="time",
+            measure_col="measure",
+            dimensions_cols=["dim0", "dim1"],
+            version=version,
+        )
+        assert len(rejected_records) == rejected_rec_num
+        df_out = wr.timestream.query(
+            f"""
+            SELECT
+                *
+            FROM "{name}"."{name}"
+            DESC LIMIT 10
+        """
+        )
+        assert df_out.shape == (3, 5)
+
+
 def test_real_csv_load_scenario(timestream_database_and_table):
     name = timestream_database_and_table
     df = pd.read_csv(