refactor(py3): Refactor all ETL scripts to using Python 3 exclusively (#1710)

* refactor(py3): Refactor all ETL scripts to using Python 3 exclusively

Fix https://github.com/linkedin/datahub/issues/1688

* Update requirements.txt

metadata-ingestion/README.md

@@ -4,14 +4,20 @@
 1. Before running any metadata ingestion job, you should make sure that DataHub backend services are all running. Easiest
 way to do that is through [Docker images](../docker).
 2. You also need to build the `mxe-schemas` module as below.
-    ```
-    ./gradlew :metadata-events:mxe-schemas:build
-    ```
-    This is needed to generate `MetadataChangeEvent.avsc` which is the schema for `MetadataChangeEvent` Kafka topic.
-3. Before launching each ETL ingestion pipeline, you can install/verify the library versions as below.
-    ```
-    pip install --user -r requirements.txt
-    ```
+   ```
+   ./gradlew :metadata-events:mxe-schemas:build
+   ```
+   This is needed to generate `MetadataChangeEvent.avsc` which is the schema for `MetadataChangeEvent` Kafka topic.
+3. All the scripts are written using Python 3 and most likely won't work with Python 2.x interpreters.
+   You can verify the version of your Python using the following command.
+   ```
+   python --version
+   ```
+   We recommend using [pyenv](https://github.com/pyenv/pyenv) to install and manage your Python environment.
+4. Before launching each ETL ingestion pipeline, you can install/verify the library versions as below.
+   ```
+   pip install --user -r requirements.txt
+   ```
     
 ## MCE Producer/Consumer CLI
 `mce_cli.py` script provides a convenient way to produce a list of MCEs from a data file. 

metadata-ingestion/hive-etl/hive_etl.py

@@ -1,6 +1,8 @@
 #! /usr/bin/python
 import sys
 import time
+from confluent_kafka import avro
+from confluent_kafka.avro import AvroProducer
 from pyhive import hive
 from TCLIService.ttypes import TOperationState
 
@@ -16,7 +18,7 @@ def hive_query(query):
     Execute the query to the HiveStore.
     """
     cursor = hive.connect(HIVESTORE).cursor()
-    cursor.execute(query, async=True)
+    cursor.execute(query, async_=True)
     status = cursor.poll().operationState
     while status in (TOperationState.INITIALIZED_STATE, TOperationState.RUNNING_STATE):
         logs = cursor.fetch_logs()
@@ -49,9 +51,6 @@ def produce_hive_dataset_mce(mce):
     """
     Produce MetadataChangeEvent records.
     """
-    from confluent_kafka import avro
-    from confluent_kafka.avro import AvroProducer
-
     conf = {'bootstrap.servers': BOOTSTRAP,
             'schema.registry.url': SCHEMAREGISTRY}
     record_schema = avro.load(AVROLOADPATH)

metadata-ingestion/hive-etl/requirements.txt

@@ -1,2 +1,4 @@
-confluent-kafka[avro]==1.1.0
-pyhive==0.6.1
+avro-python3==1.8.2
+confluent-kafka==1.4.0
+pyhive==0.6.1
+thrift-sasl==0.4.2

metadata-ingestion/kafka-etl/kafka_etl.py

@@ -2,7 +2,9 @@
 import sys
 import time
 from kazoo.client import KazooClient
-from confluent.schemaregistry.client import CachedSchemaRegistryClient
+from confluent_kafka import avro
+from confluent_kafka.avro.cached_schema_registry_client import CachedSchemaRegistryClient
+from confluent_kafka.avro import AvroProducer
 
 ZOOKEEPER='localhost:2181'
 AVROLOADPATH = '../../metadata-events/mxe-schemas/src/renamed/avro/com/linkedin/mxe/MetadataChangeEvent.avsc'
@@ -15,7 +17,7 @@ def build_kafka_dataset_mce(dataset_name, schema, schema_version):
     """
     Create the MetadataChangeEvent via dataset_name and schema.
     """
-    actor, sys_time = "urn:li:corpuser:", long(time.time())
+    actor, sys_time = "urn:li:corpuser:", time.time()
     schema_name = {"schemaName":dataset_name,"platform":"urn:li:dataPlatform:kafka","version":schema_version,"created":{"time":sys_time,"actor":actor},
                   "lastModified":{"time":sys_time,"actor":actor},"hash":"","platformSchema":{"documentSchema": schema},
                    "fields":[{"fieldPath":"","description":"","nativeDataType":"string","type":{"type":{"com.linkedin.pegasus2avro.schema.StringType":{}}}}]}
@@ -31,9 +33,6 @@ def produce_kafka_dataset_mce(mce):
     """
     Produce MetadataChangeEvent records.
     """
-    from confluent_kafka import avro
-    from confluent_kafka.avro import AvroProducer
-
     conf = {'bootstrap.servers': BOOTSTRAP,
             'schema.registry.url': SCHEMAREGISTRY}
     record_schema = avro.load(AVROLOADPATH)
@@ -58,7 +57,11 @@ for dataset_name in topics:
         continue
     topic = dataset_name + '-value'
     schema_id, schema, schema_version = client.get_latest_schema(topic)
-    print topic
+    if schema_id is None:
+      print(f"Skipping topic without schema: {topic}")
+      continue
+
+    print(topic)
     build_kafka_dataset_mce(dataset_name, str(schema), int(schema_version))
 
 sys.exit(0)

metadata-ingestion/kafka-etl/requirements.txt

@@ -1,3 +1,3 @@
-confluent-kafka[avro]==1.1.0
-python-schema-registry-client==1.2.1
+avro-python3==1.8.2
+confluent-kafka==1.4.0
 kazoo==2.5.0

metadata-ingestion/ldap-etl/ldap_etl.py

@@ -1,6 +1,8 @@
 #! /usr/bin/python
 import sys
 import ldap
+from confluent_kafka import avro
+from confluent_kafka.avro import AvroProducer
 from ldap.controls import SimplePagedResultsControl
 from distutils.version import LooseVersion
 
@@ -10,7 +12,7 @@ LDAPSERVER ='LDAPSERVER'
 BASEDN ='BASEDN'
 LDAPUSER = 'LDAPUSER'
 LDAPPASSWORD = 'LDAPPASSWORD'
-PAGESIZE = PAGESIZE
+PAGESIZE = 20
 ATTRLIST = ['cn', 'title', 'mail', 'sAMAccountName', 'department','manager']
 SEARCHFILTER='SEARCHFILTER'
 
@@ -81,9 +83,6 @@ def produce_corp_user_mce(mce):
     """
     Produce MetadataChangeEvent records
     """
-    from confluent_kafka import avro
-    from confluent_kafka.avro import AvroProducer
-
     conf = {'bootstrap.servers': BOOTSTRAP,
             'schema.registry.url': SCHEMAREGISTRY}
     record_schema = avro.load(AVROLOADPATH)

metadata-ingestion/ldap-etl/requirements.txt

@@ -1,2 +1,3 @@
-confluent-kafka[avro]==1.1.0
+avro-python3==1.8.2
+confluent-kafka==1.4.0
 python-ldap==3.2.0

metadata-ingestion/mce-cli/mce_cli.py

@@ -1,6 +1,11 @@
 #! /usr/bin/python
 import argparse
+import ast
 from confluent_kafka import avro
+from confluent_kafka.avro import AvroConsumer
+from confluent_kafka.avro import AvroProducer
+from confluent_kafka.avro.serializer import SerializerError
+
 
 topic = "MetadataChangeEvent"
 
@@ -13,9 +18,6 @@ def produce(conf, data_file, schema_record):
     """
         Produce MetadataChangeEvent records
     """
-    from confluent_kafka.avro import AvroProducer
-    import ast
-
     producer = AvroProducer(conf, default_value_schema=avro.load(schema_record))
 
     print("Producing MetadataChangeEvent records to topic {}. ^c to exit.".format(topic))
@@ -36,7 +38,7 @@ def produce(conf, data_file, schema_record):
                 break
             except ValueError as e:
                 print ("Message serialization failed {}".format(e))
-                continue
+                break
 
     print("Flushing records...")
     producer.flush()
@@ -46,9 +48,6 @@ def consume(conf, schema_record):
     """
         Consume MetadataChangeEvent records
     """
-    from confluent_kafka.avro import AvroConsumer
-    from confluent_kafka.avro.serializer import SerializerError
-
     print("Consuming MetadataChangeEvent records from topic {} with group {}. ^c to exit.".format(topic, conf["group.id"]))
 
     c = AvroConsumer(conf, reader_value_schema=avro.load(schema_record))

metadata-ingestion/mce-cli/requirements.txt

@@ -1,3 +1,2 @@
-avro-python3==1.8.2; python_version == '3.7'
-confluent-kafka==1.1.0; python_version == '3.7'
-confluent-kafka[avro]==1.1.0; python_version < '3.7'
+avro-python3==1.8.2
+confluent-kafka==1.4.0

metadata-ingestion/mysql-etl/mysql_etl.py

@@ -3,6 +3,8 @@ import sys
 import time
 import mysql.connector
 from mysql.connector import Error
+from confluent_kafka import avro
+from confluent_kafka.avro import AvroProducer
 
 HOST = 'HOST'
 DATABASE = 'DATABASE'
@@ -19,7 +21,7 @@ def build_mysql_dataset_mce(dataset_name, schema, schema_version):
     """
     Create the MetadataChangeEvent via dataset_name and schema.
     """
-    actor, fields, sys_time = "urn:li:corpuser:datahub", [], long(time.time())
+    actor, fields, sys_time = "urn:li:corpuser:datahub", [], time.time()
 
     owner = {"owners":[{"owner":actor,"type":"DATAOWNER"}],"lastModified":{"time":0,"actor":actor}}
 
@@ -41,9 +43,6 @@ def produce_mysql_dataset_mce(mce):
     """
     Produce MetadataChangeEvent records.
     """
-    from confluent_kafka import avro
-    from confluent_kafka.avro import AvroProducer
-
     conf = {'bootstrap.servers': BOOTSTRAP,
             'schema.registry.url': SCHEMAREGISTRY}
     record_schema = avro.load(AVROLOADPATH)

metadata-ingestion/mysql-etl/requirements.txt

@@ -1,2 +1,3 @@
-confluent-kafka[avro]==1.1.0
+avro-python3==1.8.2
+confluent-kafka==1.4.0
 mysql-connector==2.2.9