fix: add support for "with .. as .." statement for cleaner exception … (influxdata#218)

Author: rhajek

README.rst

@@ -220,7 +220,7 @@ Writes
 The `WriteApi <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/write_api.py>`_ supports synchronous, asynchronous and batching writes into InfluxDB 2.0.
 The data should be passed as a `InfluxDB Line Protocol <https://docs.influxdata.com/influxdb/latest/write_protocols/line_protocol_tutorial/>`_\ , `Data Point <https://github.com/influxdata/influxdb-client-python/blob/master/influxdb_client/client/write/point.py>`_ or Observable stream.
 
-**Important: The WriteApi in batching mode (default mode) is suppose to run as a singleton. To flush all your data you should call ``_write_client.close()`` at the end of your script.**
+**Important: The WriteApi in batching mode (default mode) is suppose to run as a singleton. To flush all your data you should wrap the execution using ``with client.write_api(...) as write_api:`` statement or call ``_write_client.close()`` at the end of your script.**
 
 *The default instance of WriteApi use batching.*
 
@@ -280,73 +280,69 @@ The batching is configurable by ``write_options``\ :
 
     from influxdb_client import InfluxDBClient, Point, WriteOptions
 
-    _client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")
-    _write_client = _client.write_api(write_options=WriteOptions(batch_size=500,
-                                                                 flush_interval=10_000,
-                                                                 jitter_interval=2_000,
-                                                                 retry_interval=5_000,
-                                                                 max_retries=5,
-                                                                 max_retry_delay=30_000,
-                                                                 exponential_base=2))
+    with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org") as _client:
+
+        with _client.write_api(write_options=WriteOptions(batch_size=500,
+                                                          flush_interval=10_000,
+                                                          jitter_interval=2_000,
+                                                          retry_interval=5_000,
+                                                          max_retries=5,
+                                                          max_retry_delay=30_000,
+                                                          exponential_base=2)) as _write_client:
+
+            """
+            Write Line Protocol formatted as string
+            """
+            _write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1")
+            _write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2",
+                                                        "h2o_feet,location=coyote_creek water_level=3.0 3"])
+
+            """
+            Write Line Protocol formatted as byte array
+            """
+            _write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1".encode())
+            _write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2".encode(),
+                                                        "h2o_feet,location=coyote_creek water_level=3.0 3".encode()])
+
+            """
+            Write Dictionary-style object
+            """
+            _write_client.write("my-bucket", "my-org", {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
+                                                        "fields": {"water_level": 1.0}, "time": 1})
+            _write_client.write("my-bucket", "my-org", [{"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
+                                                         "fields": {"water_level": 2.0}, "time": 2},
+                                                        {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
+                                                         "fields": {"water_level": 3.0}, "time": 3}])
+
+            """
+            Write Data Point
+            """
+            _write_client.write("my-bucket", "my-org",
+                                Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 4.0).time(4))
+            _write_client.write("my-bucket", "my-org",
+                                [Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 5.0).time(5),
+                                 Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 6.0).time(6)])
+
+            """
+            Write Observable stream
+            """
+            _data = rx \
+                .range(7, 11) \
+                .pipe(ops.map(lambda i: "h2o_feet,location=coyote_creek water_level={0}.0 {0}".format(i)))
+
+            _write_client.write("my-bucket", "my-org", _data)
+
+            """
+            Write Pandas DataFrame
+            """
+            _now = datetime.now(UTC)
+            _data_frame = pd.DataFrame(data=[["coyote_creek", 1.0], ["coyote_creek", 2.0]],
+                                       index=[_now, _now + timedelta(hours=1)],
+                                       columns=["location", "water_level"])
+
+            _write_client.write("my-bucket", "my-org", record=_data_frame, data_frame_measurement_name='h2o_feet',
+                                data_frame_tag_columns=['location'])
 
-    """
-    Write Line Protocol formatted as string
-    """
-    _write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1")
-    _write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2",
-                                                "h2o_feet,location=coyote_creek water_level=3.0 3"])
-
-    """
-    Write Line Protocol formatted as byte array
-    """
-    _write_client.write("my-bucket", "my-org", "h2o_feet,location=coyote_creek water_level=1.0 1".encode())
-    _write_client.write("my-bucket", "my-org", ["h2o_feet,location=coyote_creek water_level=2.0 2".encode(),
-                                                "h2o_feet,location=coyote_creek water_level=3.0 3".encode()])
-
-    """
-    Write Dictionary-style object
-    """
-    _write_client.write("my-bucket", "my-org", {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
-                                                "fields": {"water_level": 1.0}, "time": 1})
-    _write_client.write("my-bucket", "my-org", [{"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
-                                                 "fields": {"water_level": 2.0}, "time": 2},
-                                                {"measurement": "h2o_feet", "tags": {"location": "coyote_creek"},
-                                                 "fields": {"water_level": 3.0}, "time": 3}])
-
-    """
-    Write Data Point
-    """
-    _write_client.write("my-bucket", "my-org",
-                        Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 4.0).time(4))
-    _write_client.write("my-bucket", "my-org",
-                        [Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 5.0).time(5),
-                         Point("h2o_feet").tag("location", "coyote_creek").field("water_level", 6.0).time(6)])
-
-    """
-    Write Observable stream
-    """
-    _data = rx \
-        .range(7, 11) \
-        .pipe(ops.map(lambda i: "h2o_feet,location=coyote_creek water_level={0}.0 {0}".format(i)))
-
-    _write_client.write("my-bucket", "my-org", _data)
-
-    """
-    Write Pandas DataFrame
-    """
-    _now = datetime.now(UTC)
-    _data_frame = pd.DataFrame(data=[["coyote_creek", 1.0], ["coyote_creek", 2.0]],
-                               index=[_now, _now + timedelta(hours=1)],
-                               columns=["location", "water_level"])
-
-    _write_client.write("my-bucket", "my-org", record=_data_frame, data_frame_measurement_name='h2o_feet',
-                        data_frame_tag_columns=['location'])
-
-    """
-    Close client
-    """
-    _write_client.close()
-    _client.close()
 
 
 Default Tags

examples/buckets_management.py

@@ -10,39 +10,38 @@
 url = "http://localhost:8086"
 token = "my-token"
 
-client = InfluxDBClient(url=url, token=token)
-buckets_api = client.buckets_api()
+with InfluxDBClient(url=url, token=token) as client:
+    buckets_api = client.buckets_api()
+
+    """
+    The Bucket API uses as a parameter the Organization ID. We have to retrieve ID by Organization API.
+    """
+    org_name = "my-org"
+    org = list(filter(lambda it: it.name == org_name, client.organizations_api().find_organizations()))[0]
+
+    """
+    Create Bucket with retention policy set to 3600 seconds and name "bucket-by-python"
+    """
+    print(f"------- Create -------\n")
+    retention_rules = BucketRetentionRules(type="expire", every_seconds=3600)
+    created_bucket = buckets_api.create_bucket(bucket_name="bucket-by-python",
+                                               retention_rules=retention_rules,
+                                               org_id=org.id)
+    print(created_bucket)
+
+    """
+    List all Buckets
+    """
+    print(f"\n------- List -------\n")
+    buckets = buckets_api.find_buckets().buckets
+    print("\n".join([f" ---\n ID: {bucket.id}\n Name: {bucket.name}\n Retention: {bucket.retention_rules}"
+                     for bucket in buckets]))
+    print("---")
+
+    """
+    Delete previously created bucket
+    """
+    print(f"------- Delete -------\n")
+    buckets_api.delete_bucket(created_bucket)
+    print(f" successfully deleted bucket: {created_bucket.name}")
 
-"""
-The Bucket API uses as a parameter the Organization ID. We have to retrieve ID by Organization API.
-"""
-org_name = "my-org"
-org = list(filter(lambda it: it.name == org_name, client.organizations_api().find_organizations()))[0]
-
-"""
-Create Bucket with retention policy set to 3600 seconds and name "bucket-by-python"
-"""
-print(f"------- Create -------\n")
-retention_rules = BucketRetentionRules(type="expire", every_seconds=3600)
-created_bucket = buckets_api.create_bucket(bucket_name="bucket-by-python",
-                                           retention_rules=retention_rules,
-                                           org_id=org.id)
-print(created_bucket)
-
-"""
-List all Buckets
-"""
-print(f"\n------- List -------\n")
-buckets = buckets_api.find_buckets().buckets
-print("\n".join([f" ---\n ID: {bucket.id}\n Name: {bucket.name}\n Retention: {bucket.retention_rules}"
-                 for bucket in buckets]))
-print("---")
-
-"""
-Delete previously created bucket
-"""
-print(f"------- Delete -------\n")
-buckets_api.delete_bucket(created_bucket)
-print(f" successfully deleted bucket: {created_bucket.name}")
-
-client.close()

examples/example.py

@@ -4,39 +4,37 @@
 from influxdb_client import WritePrecision, InfluxDBClient, Point
 from influxdb_client.client.write_api import SYNCHRONOUS
 
-bucket = "my-bucket"
-
-client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org")
-
-write_api = client.write_api(write_options=SYNCHRONOUS)
-query_api = client.query_api()
-
-p = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3).time(datetime.now(), WritePrecision.MS)
-
-# write using point structure
-write_api.write(bucket=bucket, record=p)
-
-line_protocol = p.to_line_protocol()
-print(line_protocol)
-
-# write using line protocol string
-write_api.write(bucket=bucket, record=line_protocol)
-
-# using Table structure
-tables = query_api.query('from(bucket:"my-bucket") |> range(start: -1m)')
-for table in tables:
-    print(table)
-    for record in table.records:
-        # process record
-        print(record.values)
-
-# using csv library
-csv_result = query_api.query_csv('from(bucket:"my-bucket") |> range(start: -10m)')
-val_count = 0
-for record in csv_result:
-    for cell in record:
-        val_count += 1
-print("val count: ", val_count)
-
-response = query_api.query_raw('from(bucket:"my-bucket") |> range(start: -10m)')
-print (codecs.decode(response.data))
+with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", debug=False) as client:
+    query_api = client.query_api()
+
+    p = Point("my_measurement").tag("location", "Prague").field("temperature", 25.3).time(datetime.utcnow(),
+                                                                                          WritePrecision.MS)
+    write_api = client.write_api(write_options=SYNCHRONOUS)
+
+    # write using point structure
+    write_api.write(bucket="my-bucket", record=p)
+
+    line_protocol = p.to_line_protocol()
+    print(line_protocol)
+
+    # write using line protocol string
+    write_api.write(bucket="my-bucket", record=line_protocol)
+
+    # using Table structure
+    tables = query_api.query('from(bucket:"my-bucket") |> range(start: -10m)')
+    for table in tables:
+        print(table)
+        for record in table.records:
+            # process record
+            print(record.values)
+
+    # using csv library
+    csv_result = query_api.query_csv('from(bucket:"my-bucket") |> range(start: -10m)')
+    val_count = 0
+    for record in csv_result:
+        for cell in record:
+            val_count += 1
+    print("val count: ", val_count)
+
+    response = query_api.query_raw('from(bucket:"my-bucket") |> range(start: -10m)')
+    print (codecs.decode(response.data))

examples/import_data_set.py

@@ -60,39 +60,33 @@ def parse_row(row: OrderedDict):
     .from_iterable(DictReader(open('vix-daily.csv', 'r'))) \
     .pipe(ops.map(lambda row: parse_row(row)))
 
-client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", debug=True)
+with InfluxDBClient(url="http://localhost:8086", token="my-token", org="my-org", debug=True) as client:
 
-"""
-Create client that writes data in batches with 50_000 items.
-"""
-write_api = client.write_api(write_options=WriteOptions(batch_size=50_000, flush_interval=10_000))
-
-"""
-Write data into InfluxDB
-"""
-write_api.write(bucket="my-bucket", record=data)
-write_api.close()
+    """
+    Create client that writes data in batches with 50_000 items.
+    """
+    with client.write_api(write_options=WriteOptions(batch_size=50_000, flush_interval=10_000)) as write_api:
 
-"""
-Querying max value of CBOE Volatility Index
-"""
-query = 'from(bucket:"my-bucket")' \
-        ' |> range(start: 0, stop: now())' \
-        ' |> filter(fn: (r) => r._measurement == "financial-analysis")' \
-        ' |> max()'
-result = client.query_api().query(query=query)
+        """
+        Write data into InfluxDB
+        """
+        write_api.write(bucket="my-bucket", record=data)
 
-"""
-Processing results
-"""
-print()
-print("=== results ===")
-print()
-for table in result:
-    for record in table.records:
-        print('max {0:5} = {1}'.format(record.get_field(), record.get_value()))
+    """
+    Querying max value of CBOE Volatility Index
+    """
+    query = 'from(bucket:"my-bucket")' \
+            ' |> range(start: 0, stop: now())' \
+            ' |> filter(fn: (r) => r._measurement == "financial-analysis")' \
+            ' |> max()'
+    result = client.query_api().query(query=query)
 
-"""
-Close client
-"""
-client.close()
+    """
+    Processing results
+    """
+    print()
+    print("=== results ===")
+    print()
+    for table in result:
+        for record in table.records:
+            print('max {0:5} = {1}'.format(record.get_field(), record.get_value()))