Add cloudwatchlogs module and bumping version to 0.0.1! 🚀

README.md

@@ -1,4 +1,4 @@
-# AWS Data Wrangler (beta)
+# AWS Data Wrangler
 
 > Utility belt to handle data on AWS.
 
@@ -14,16 +14,28 @@
 
 ## Use Cases
 
-* Pandas -> Parquet (S3)
-* Pandas -> CSV (S3)
+### Pandas
+* Pandas -> Parquet (S3) (Parallel :rocket:)
+* Pandas -> CSV (S3) (Parallel :rocket:)
 * Pandas -> Glue Catalog
-* Pandas -> Athena
-* Pandas -> Redshift
+* Pandas -> Athena (Parallel :rocket:)
+* Pandas -> Redshift (Parallel :rocket:)
 * CSV (S3) -> Pandas (One shot or Batching)
 * Athena -> Pandas (One shot or Batching)
-* PySpark -> Redshift
-* Delete S3 objects (parallel :rocket:)
-* Encrypt S3 data with KMS keys
+* CloudWatch Logs Insights -> Pandas (NEW :star:)
+* Encrypt Pandas Dataframes on S3 with KMS keys (NEW :star:)
+
+### PySpark
+* PySpark -> Redshift (Parallel :rocket:) (NEW :star:)
+
+### General
+* List S3 objects (Parallel :rocket:)
+* Delete S3 objects (Parallel :rocket:)
+* Delete listed S3 objects (Parallel :rocket:)
+* Delete NOT listed S3 objects (Parallel :rocket:)
+* Copy listed S3 objects (Parallel :rocket:)
+* Get the size of S3 objects (Parallel :rocket:)
+* Get CloudWatch Logs Insights query results (NEW :star:)
 
 ## Installation
 
@@ -37,7 +49,9 @@ Runs anywhere (AWS Lambda, AWS Glue, EMR, EC2, on-premises, local, etc).
 
 ## Examples
 
-### Writing Pandas Dataframe to S3 + Glue Catalog
+### Pandas
+
+#### Writing Pandas Dataframe to S3 + Glue Catalog
 
 ```py3
 session = awswrangler.Session()
@@ -51,7 +65,7 @@ session.pandas.to_parquet(
 
 If a Glue Database name is passed, all the metadata will be created in the Glue Catalog. If not, only the s3 data write will be done.
 
-### Writing Pandas Dataframe to S3 as Parquet encrypting with a KMS key
+#### Writing Pandas Dataframe to S3 as Parquet encrypting with a KMS key
 
 ```py3
 extra_args = {
@@ -64,7 +78,7 @@ session.pandas.to_parquet(
 )
 ```
 
-### Reading from AWS Athena to Pandas
+#### Reading from AWS Athena to Pandas
 
 ```py3
 session = awswrangler.Session()
@@ -74,7 +88,7 @@ dataframe = session.pandas.read_sql_athena(
 )
 ```
 
-### Reading from AWS Athena to Pandas in chunks (For memory restrictions)
+#### Reading from AWS Athena to Pandas in chunks (For memory restrictions)
 
 ```py3
 session = awswrangler.Session()
@@ -87,14 +101,14 @@ for dataframe in dataframe_iter:
     print(dataframe)  # Do whatever you want
 ```
 
-### Reading from S3 (CSV) to Pandas
+#### Reading from S3 (CSV) to Pandas
 
 ```py3
 session = awswrangler.Session()
 dataframe = session.pandas.read_csv(path="s3://...")
 ```
 
-### Reading from S3 (CSV) to Pandas in chunks (For memory restrictions)
+#### Reading from S3 (CSV) to Pandas in chunks (For memory restrictions)
 
 ```py3
 session = awswrangler.Session()
@@ -106,7 +120,17 @@ for dataframe in dataframe_iter:
     print(dataframe)  # Do whatever you want
 ```
 
-### Typical Pandas ETL
+#### Reading from CloudWatch Logs Insights to Pandas
+
+```py3
+session = awswrangler.Session()
+dataframe = session.pandas.read_log_query(
+    log_group_names=[LOG_GROUP_NAME],
+    query="fields @timestamp, @message | sort @timestamp desc | limit 5",
+)
+```
+
+#### Typical Pandas ETL
 
 ```py3
 import pandas
@@ -125,7 +149,9 @@ session.pandas.to_parquet(  # Storing the data and metadata to Data Lake
 )
 ```
 
-### Loading Pyspark Dataframe to Redshift
+### PySpark
+
+#### Loading PySpark Dataframe to Redshift
 
 ```py3
 session = awswrangler.Session(spark_session=spark)
@@ -140,13 +166,25 @@ session.spark.to_redshift(
 )
 ```
 
-### Deleting a bunch of S3 objects
+### General
+
+#### Deleting a bunch of S3 objects (parallel :rocket:)
 
 ```py3
 session = awswrangler.Session()
 session.s3.delete_objects(path="s3://...")
 ```
 
+#### Get CloudWatch Logs Insights query results
+
+```py3
+session = awswrangler.Session()
+results = session.cloudwatchlogs.query(
+    log_group_names=[LOG_GROUP_NAME],
+    query="fields @timestamp, @message | sort @timestamp desc | limit 5",
+)
+```
+
 ## Diving Deep
 
 ### Pandas to Redshift Flow

awswrangler/__init__.py

@@ -6,6 +6,7 @@
 from awswrangler.pandas import Pandas  # noqa
 from awswrangler.s3 import S3  # noqa
 from awswrangler.athena import Athena  # noqa
+from awswrangler.cloudwatchlogs import CloudWatchLogs  # noqa
 from awswrangler.glue import Glue  # noqa
 from awswrangler.redshift import Redshift  # noqa
 import awswrangler.utils  # noqa

awswrangler/__version__.py

@@ -1,4 +1,4 @@
 __title__ = "awswrangler"
 __description__ = "Utility belt to handle data on AWS."
-__version__ = "0.0b32"
+__version__ = "0.0.1"
 __license__ = "Apache License 2.0"

awswrangler/athena.py

@@ -1,7 +1,7 @@
 from time import sleep
 import logging
 
-from awswrangler.exceptions import UnsupportedType
+from awswrangler.exceptions import UnsupportedType, QueryFailed, QueryCancelled
 
 logger = logging.getLogger(__name__)
 
@@ -86,12 +86,29 @@ def run_query(self, query, database, s3_output=None):
         return response["QueryExecutionId"]
 
     def wait_query(self, query_execution_id):
+        """
+        Wait query ends
+
+        :param query_execution_id: Query execution ID
+        :return: Query response
+        """
         final_states = ["FAILED", "SUCCEEDED", "CANCELLED"]
         response = self._client_athena.get_query_execution(
             QueryExecutionId=query_execution_id)
-        while (response.get("QueryExecution").get("Status").get("State") not in
-               final_states):
+        state = response["QueryExecution"]["Status"]["State"]
+        while state not in final_states:
             sleep(QUERY_WAIT_POLLING_DELAY)
             response = self._client_athena.get_query_execution(
                 QueryExecutionId=query_execution_id)
+            state = response["QueryExecution"]["Status"]["State"]
+        logger.debug(f"state: {state}")
+        logger.debug(
+            f"StateChangeReason: {response['QueryExecution']['Status'].get('StateChangeReason')}"
+        )
+        if state == "FAILED":
+            raise QueryFailed(
+                response["QueryExecution"]["Status"].get("StateChangeReason"))
+        elif state == "CANCELLED":
+            raise QueryCancelled(
+                response["QueryExecution"]["Status"].get("StateChangeReason"))
         return response

awswrangler/cloudwatchlogs.py

@@ -0,0 +1,94 @@
+from time import sleep
+from datetime import datetime
+import logging
+
+from awswrangler.exceptions import QueryFailed, QueryCancelled
+
+logger = logging.getLogger(__name__)
+
+QUERY_WAIT_POLLING_DELAY = 0.2  # MILLISECONDS
+
+
+class CloudWatchLogs:
+    def __init__(self, session):
+        self._session = session
+        self._client_logs = session.boto3_session.client(
+            service_name="logs", config=session.botocore_config)
+
+    def start_query(self,
+                    query,
+                    log_group_names,
+                    start_time=datetime(year=1970, month=1, day=1),
+                    end_time=datetime.utcnow(),
+                    limit=None):
+        """
+        Run a query against AWS CloudWatchLogs Insights and wait the results
+        https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/CWL_QuerySyntax.html
+
+        :param query: The query string to use.
+        :param log_group_names: The list of log groups to be queried. You can include up to 20 log groups.
+        :param start_time: The beginning of the time range to query (datetime.datetime object)
+        :param end_time: The end of the time range to query (datetime.datetime object)
+        :param limit: The maximum number of log events to return in the query.
+        :return: Query ID
+        """
+        start_timestamp = int(1000 * start_time.timestamp())
+        end_timestamp = int(1000 * end_time.timestamp())
+        logger.debug(f"start_timestamp: {start_timestamp}")
+        logger.debug(f"end_timestamp: {end_timestamp}")
+        args = {
+            "logGroupNames": log_group_names,
+            "startTime": start_timestamp,
+            "endTime": end_timestamp,
+            "queryString": query
+        }
+        if limit:
+            args["limit"] = limit
+        response = self._client_logs.start_query(**args)
+        return response["queryId"]
+
+    def wait_query(self, query_id):
+        """
+        Wait query ends
+
+        :param query_id: Query ID
+        :return: Query results
+        """
+        final_states = ["Complete", "Failed", "Cancelled"]
+        response = self._client_logs.get_query_results(queryId=query_id)
+        status = response["status"]
+        while status not in final_states:
+            sleep(QUERY_WAIT_POLLING_DELAY)
+            response = self._client_logs.get_query_results(queryId=query_id)
+            status = response["status"]
+        logger.debug(f"status: {status}")
+        if status == "Failed":
+            raise QueryFailed(f"query ID: {query_id}")
+        elif status == "Cancelled":
+            raise QueryCancelled(f"query ID: {query_id}")
+        return response
+
+    def query(self,
+              query,
+              log_group_names,
+              start_time=datetime(year=1970, month=1, day=1),
+              end_time=datetime.utcnow(),
+              limit=None):
+        """
+        Run a query against AWS CloudWatchLogs Insights and wait the results
+        https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/CWL_QuerySyntax.html
+
+        :param query: The query string to use.
+        :param log_group_names: The list of log groups to be queried. You can include up to 20 log groups.
+        :param start_time: The beginning of the time range to query (datetime.datetime object)
+        :param end_time: The end of the time range to query (datetime.datetime object)
+        :param limit: The maximum number of log events to return in the query.
+        :return: Results
+        """
+        query_id = self.start_query(query=query,
+                                    log_group_names=log_group_names,
+                                    start_time=start_time,
+                                    end_time=end_time,
+                                    limit=limit)
+        response = self.wait_query(query_id=query_id)
+        return response["results"]

awswrangler/exceptions.py

@@ -56,3 +56,11 @@ class InvalidRedshiftSortkey(Exception):
 
 class EmptyDataframe(Exception):
     pass
+
+
+class QueryCancelled(Exception):
+    pass
+
+
+class QueryFailed(Exception):
+    pass

awswrangler/pandas.py

@@ -4,6 +4,7 @@
 from math import floor
 import copy
 import csv
+from datetime import datetime
 
 import pandas
 import pyarrow
@@ -833,3 +834,37 @@ def to_redshift(
             mode=mode,
         )
         self._session.s3.delete_objects(path=path)
+
+    def read_log_query(self,
+                       query,
+                       log_group_names,
+                       start_time=datetime(year=1970, month=1, day=1),
+                       end_time=datetime.utcnow(),
+                       limit=None):
+        """
+        Run a query against AWS CloudWatchLogs Insights and convert the results to Pandas DataFrame
+
+        :param query: The query string to use. https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/CWL_QuerySyntax.html
+        :param log_group_names: The list of log groups to be queried. You can include up to 20 log groups.
+        :param start_time: The beginning of the time range to query (datetime.datetime object)
+        :param end_time: The end of the time range to query (datetime.datetime object)
+        :param limit: The maximum number of log events to return in the query. If the query string uses the fields command, only the specified fields and their values are returned.
+        :return: Results as a Pandas DataFrame
+        """
+        results = self._session.cloudwatchlogs.query(
+            query=query,
+            log_group_names=log_group_names,
+            start_time=start_time,
+            end_time=end_time,
+            limit=limit)
+        pre_df = []
+        for row in results:
+            new_row = {}
+            for col in row:
+                if col["field"].startswith("@"):
+                    col_name = col["field"].replace("@", "", 1)
+                else:
+                    col_name = col["field"]
+                new_row[col_name] = col["value"]
+            pre_df.append(new_row)
+        return pandas.DataFrame(pre_df)

awswrangler/session.py

@@ -7,6 +7,7 @@
 
 from awswrangler.s3 import S3
 from awswrangler.athena import Athena
+from awswrangler.cloudwatchlogs import CloudWatchLogs
 from awswrangler.pandas import Pandas
 from awswrangler.glue import Glue
 from awswrangler.redshift import Redshift
@@ -88,6 +89,7 @@ def __init__(
             self._load_new_boto3_session()
         self._s3 = None
         self._athena = None
+        self._cloudwatchlogs = None
         self._pandas = None
         self._glue = None
         self._redshift = None
@@ -202,6 +204,12 @@ def athena(self):
             self._athena = Athena(session=self)
         return self._athena
 
+    @property
+    def cloudwatchlogs(self):
+        if not self._cloudwatchlogs:
+            self._cloudwatchlogs = CloudWatchLogs(session=self)
+        return self._cloudwatchlogs
+
     @property
     def pandas(self):
         if not self._pandas:

building/Dockerfile

@@ -1,4 +1,4 @@
-FROM lambci/lambda:build-python3.6
+FROM lambci/lambda:build-python3.7
 
 RUN pip install --upgrade pip