From 797da3a8a50cbd9b3f09677fc3e1639ffbad7187 Mon Sep 17 00:00:00 2001
From: Jim Park <jimp@scribd.com>
Date: Tue, 31 Mar 2020 16:36:14 -0700
Subject: [PATCH] feat: Update AWS DD Forwarder to v3.5.0

COREINF-1665
---
 README.md                        |  18 +-
 files/aws-dd-forwarder-3.5.0.zip | Bin 0 -> 24469 bytes
 files/dd_log_lambda.zip          | Bin 8063 -> 0 bytes
 files/lambda_function.py         | 865 -------------------------------
 logshipping.tf                   |   6 +-
 5 files changed, 15 insertions(+), 874 deletions(-)
 create mode 100644 files/aws-dd-forwarder-3.5.0.zip
 delete mode 100644 files/dd_log_lambda.zip
 delete mode 100644 files/lambda_function.py

diff --git a/README.md b/README.md
index e368c51..3e93d3f 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,17 @@
 # terraform-aws-datadog
 
-Terraform module which sets up various AWS / Datadog integrations including:
-
-- Configure Datadog's builtin AWS integration
-- Configure Cloudtrail logshipping
-- Create ELB S3 bucket for logs and logshipping
-- Sync Cloudwatch logs for a given list of log groups
+This module configures the AWS / Datadog integration.
+
+There are two main components:
+
+1. Datadog core integration, enabling [datadog's AWS integration](https://docs.datadoghq.com/integrations/amazon_web_services/)
+2. Datadog log forwarder, enabling [logshipping watched S3 buckets](https://github.com/DataDog/datadog-serverless-functions/tree/master/aws/logs_monitoring)
+  - Forward CloudWatch, ELB, S3, CloudTrail, VPC and CloudFront logs to Datadog
+  - Forward S3 events to Datadog
+  - Forward Kinesis data stream events to Datadog, only CloudWatch logs are supported
+  - Forward custom metrics from AWS Lambda functions via CloudWatch logs
+  - Forward traces from AWS Lambda functions via CloudWatch logs
+  - Generate and submit enhanced Lambda metrics (aws.lambda.enhanced.*) parsed from the AWS REPORT log: duration, billed_duration, max_memory_used, and estimated_cost
 
 
 ## Usage
diff --git a/files/aws-dd-forwarder-3.5.0.zip b/files/aws-dd-forwarder-3.5.0.zip
new file mode 100644
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diff --git a/files/lambda_function.py b/files/lambda_function.py
deleted file mode 100644
index 569e078..0000000
--- a/files/lambda_function.py
+++ /dev/null
@@ -1,865 +0,0 @@
-# Unless explicitly stated otherwise all files in this repository are licensed
-# under the Apache License Version 2.0.
-# This product includes software developed at Datadog (https://www.datadoghq.com/).
-# Copyright 2018 Datadog, Inc.
-
-from __future__ import print_function
-
-import base64
-import gzip
-import json
-import os
-import re
-import socket
-from botocore.vendored import requests
-import time
-import ssl
-import urllib
-import itertools
-from io import BytesIO, BufferedReader
-
-import boto3
-
-try:
-    # Datadog Lambda layer is required to forward metrics
-    from datadog_lambda.wrapper import datadog_lambda_wrapper
-    from datadog_lambda.metric import lambda_stats
-    DD_FORWARD_METRIC = True
-except ImportError:
-    # For backward-compatibility
-    DD_FORWARD_METRIC = False
-
-
-# Set this variable to `False` to disable log forwarding.
-# E.g., when you only want to forward metrics from logs.
-DD_FORWARD_LOG = os.getenv("DD_FORWARD_LOG", default="true").lower() == "true"
-
-
-# Change this value to change the underlying network client (HTTP or TCP),
-# by default, use the TCP client.
-DD_USE_TCP = os.getenv("DD_USE_TCP", default="true").lower() == "true"
-
-
-# Define the destination endpoint to send logs to
-DD_SITE = os.getenv("DD_SITE", default="datadoghq.com")
-if DD_USE_TCP:
-    DD_URL = os.getenv("DD_URL", default="lambda-intake.logs." + DD_SITE)
-    try:
-        if "DD_SITE" in os.environ and DD_SITE == "datadoghq.eu":
-            DD_PORT = int(os.environ.get("DD_PORT", 443))
-        else:
-            DD_PORT = int(os.environ.get("DD_PORT", 10516))
-    except Exception:
-        DD_PORT = 10516
-else:
-    DD_URL = os.getenv("DD_URL", default="lambda-http-intake.logs." + DD_SITE)
-
-
-class ScrubbingRuleConfig(object):
-    def __init__(self, name, pattern, placeholder):
-        self.name = name
-        self.pattern = pattern
-        self.placeholder = placeholder
-
-
-# Scrubbing sensitive data
-# Option to redact all pattern that looks like an ip address / email address / custom pattern
-SCRUBBING_RULE_CONFIGS = [
-    ScrubbingRuleConfig(
-        "REDACT_IP", "\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", "xxx.xxx.xxx.xxx"
-    ),
-    ScrubbingRuleConfig(
-        "REDACT_EMAIL",
-        "[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+",
-        "xxxxx@xxxxx.com",
-    ),
-    ScrubbingRuleConfig(
-        "DD_SCRUBBING_RULE", 
-        os.getenv("DD_SCRUBBING_RULE", default=None), 
-        os.getenv("DD_SCRUBBING_RULE_REPLACEMENT", default="xxxxx")
-    )
-]
-
-# Use for include, exclude, and scrubbing rules
-def compileRegex(rule, pattern):
-    if pattern is not None:
-        if pattern == "":
-            # If pattern is an empty string, raise exception
-            raise Exception("No pattern provided:\nAdd pattern or remove {} environment variable".format(rule))
-        try:
-            return re.compile(pattern)
-        except Exception:
-            raise Exception("could not compile {} regex with pattern: {}".format(rule, pattern))
-
-
-# Filtering logs 
-# Option to include or exclude logs based on a pattern match
-INCLUDE_AT_MATCH = os.getenv("INCLUDE_AT_MATCH", default=None)
-include_regex = compileRegex("INCLUDE_AT_MATCH", INCLUDE_AT_MATCH)
-
-EXCLUDE_AT_MATCH = os.getenv("EXCLUDE_AT_MATCH", default=None)
-exclude_regex = compileRegex("EXCLUDE_AT_MATCH", EXCLUDE_AT_MATCH)
-
-
-# DD_API_KEY: Datadog API Key
-DD_API_KEY = "<your_api_key>"
-if "DD_KMS_API_KEY" in os.environ:
-    ENCRYPTED = os.environ["DD_KMS_API_KEY"]
-    DD_API_KEY = boto3.client("kms").decrypt(
-        CiphertextBlob=base64.b64decode(ENCRYPTED)
-    )["Plaintext"]
-elif "DD_API_KEY" in os.environ:
-    DD_API_KEY = os.environ["DD_API_KEY"]
-
-# Strip any trailing and leading whitespace from the API key
-DD_API_KEY = DD_API_KEY.strip()
-
-# DD_API_KEY must be set
-if DD_API_KEY == "<your_api_key>" or DD_API_KEY == "":
-    raise Exception(
-        "You must configure your Datadog API key using "
-        "DD_KMS_API_KEY or DD_API_KEY"
-    )
-# Check if the API key is the correct number of characters
-if len(DD_API_KEY) != 32:
-    raise Exception(
-        "The API key is not the expected length. "
-        "Please confirm that your API key is correct"
-    )
-# Validate the API key
-validation_res = requests.get(
-    "https://api.{}/api/v1/validate?api_key={}".format(DD_SITE, DD_API_KEY)
-)
-if not validation_res.ok:
-    raise Exception("The API key is not valid.")
-
-
-# DD_MULTILINE_LOG_REGEX_PATTERN: Datadog Multiline Log Regular Expression Pattern
-DD_MULTILINE_LOG_REGEX_PATTERN = None
-if "DD_MULTILINE_LOG_REGEX_PATTERN" in os.environ:
-    DD_MULTILINE_LOG_REGEX_PATTERN = os.environ["DD_MULTILINE_LOG_REGEX_PATTERN"]
-    try:
-        multiline_regex = re.compile(
-            "(?<!^)\s+(?={})(?!.\s)".format(DD_MULTILINE_LOG_REGEX_PATTERN)
-        )
-    except Exception:
-        raise Exception("could not compile multiline regex with pattern: {}".format(DD_MULTILINE_LOG_REGEX_PATTERN))
-    multiline_regex_start_pattern = re.compile(
-        "^{}".format(DD_MULTILINE_LOG_REGEX_PATTERN)
-    )
-
-rds_regex = re.compile("/aws/rds/instance/(?P<host>[^/]+)/(?P<name>[^/]+)")
-
-DD_SOURCE = "ddsource"
-DD_CUSTOM_TAGS = "ddtags"
-DD_SERVICE = "service"
-DD_HOST = "host"
-DD_FORWARDER_VERSION = "1.4.1"
-
-# Pass custom tags as environment variable, ensure comma separated, no trailing comma in envvar!
-DD_TAGS = os.environ.get("DD_TAGS", "")
-
-
-class RetriableException(Exception):
-    pass
-
-
-class ScrubbingException(Exception):
-    pass
-
-
-class DatadogClient(object):
-    """
-    Client that implements a exponential retrying logic to send a batch of logs.
-    """
-
-    def __init__(self, client, max_backoff=30):
-        self._client = client
-        self._max_backoff = max_backoff
-
-    def send(self, logs):
-        backoff = 1
-        while True:
-            try:
-                self._client.send(logs)
-                return
-            except RetriableException:
-                time.sleep(backoff)
-                if backoff < self._max_backoff:
-                    backoff *= 2
-                continue
-
-    def __enter__(self):
-        self._client.__enter__()
-        return self
-
-    def __exit__(self, ex_type, ex_value, traceback):
-        self._client.__exit__(ex_type, ex_value, traceback)
-
-
-class DatadogTCPClient(object):
-    """
-    Client that sends a batch of logs over TCP.
-    """
-
-    def __init__(self, host, port, api_key, scrubber):
-        self.host = host
-        self.port = port
-        self._api_key = api_key
-        self._scrubber = scrubber
-        self._sock = None
-
-    def _connect(self):
-        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-        sock = ssl.wrap_socket(sock)
-        sock.connect((self.host, self.port))
-        self._sock = sock
-
-    def _close(self):
-        if self._sock:
-            self._sock.close()
-
-    def _reset(self):
-        self._close()
-        self._connect()
-
-    def send(self, logs):
-        try:
-            frame = self._scrubber.scrub(
-                "".join(
-                    ["{} {}\n".format(self._api_key, log) for log in logs]
-                )
-            )
-            self._sock.sendall(frame.encode("UTF-8"))
-        except ScrubbingException:
-            raise Exception("could not scrub the payload")
-        except Exception:
-            # most likely a network error, reset the connection
-            self._reset()
-            raise RetriableException()
-
-    def __enter__(self):
-        self._connect()
-        return self
-
-    def __exit__(self, ex_type, ex_value, traceback):
-        self._close()
-
-
-class DatadogHTTPClient(object):
-    """
-    Client that sends a batch of logs over HTTP.
-    """
-
-    _POST = "POST"
-    _HEADERS = {"Content-type": "application/json"}
-
-    def __init__(self, host, api_key, scrubber, timeout=10):
-        self._url = "https://{}/v1/input/{}".format(host, api_key)
-        self._scrubber = scrubber
-        self._timeout = timeout
-        self._session = None
-
-    def _connect(self):
-        self._session = requests.Session()
-        self._session.headers.update(self._HEADERS)
-
-    def _close(self):
-        self._session.close()
-
-    def send(self, logs):
-        """
-        Sends a batch of log, only retry on server and network errors.
-        """
-        try:
-            resp = self._session.post(
-                self._url,
-                data=self._scrubber.scrub("[{}]".format(",".join(logs))),
-                timeout=self._timeout,
-            )
-        except ScrubbingException:
-            raise Exception("could not scrub the payload")
-        except Exception:
-            # most likely a network error
-            raise RetriableException()
-        if resp.status_code >= 500:
-            # server error
-            raise RetriableException()
-        elif resp.status_code >= 400:
-            # client error
-            raise Exception(
-                "client error, status: {}, reason {}".format(
-                    resp.status_code, resp.reason
-                )
-            )
-        else:
-            # success
-            return
-
-    def __enter__(self):
-        self._connect()
-        return self
-
-    def __exit__(self, ex_type, ex_value, traceback):
-        self._close()
-
-
-class DatadogBatcher(object):
-    def __init__(self, max_log_size_bytes, max_size_bytes, max_size_count):
-        self._max_log_size_bytes = max_log_size_bytes
-        self._max_size_bytes = max_size_bytes
-        self._max_size_count = max_size_count
-
-    def _sizeof_bytes(self, log):
-        return len(log.encode("UTF-8"))
-
-    def batch(self, logs):
-        """
-        Returns an array of batches.
-        Each batch contains at most max_size_count logs and
-        is not strictly greater than max_size_bytes.
-        All logs strictly greater than max_log_size_bytes are dropped.
-        """
-        batches = []
-        batch = []
-        size_bytes = 0
-        size_count = 0
-        for log in logs:
-            log_size_bytes = self._sizeof_bytes(log)
-            if size_count > 0 and (
-                size_count >= self._max_size_count
-                or size_bytes + log_size_bytes > self._max_size_bytes
-            ):
-                batches.append(batch)
-                batch = []
-                size_bytes = 0
-                size_count = 0
-            # all logs exceeding max_log_size_bytes are dropped here
-            if log_size_bytes <= self._max_log_size_bytes:
-                batch.append(log)
-                size_bytes += log_size_bytes
-                size_count += 1
-        if size_count > 0:
-            batches.append(batch)
-        return batches
-
-
-class ScrubbingRule(object):
-    def __init__(self, regex, placeholder):
-        self.regex = regex
-        self.placeholder = placeholder
-
-
-class DatadogScrubber(object):
-    def __init__(self, configs):
-        rules = []
-        for config in configs:
-            if config.name in os.environ:
-                rules.append(
-                    ScrubbingRule(
-                        compileRegex(config.name, config.pattern), 
-                        config.placeholder
-                    )
-                )
-        self._rules = rules
-
-    def scrub(self, payload):
-        for rule in self._rules:
-            try:
-                payload = rule.regex.sub(rule.placeholder, payload)
-            except Exception:
-                raise ScrubbingException()
-        return payload
-
-
-def datadog_forwarder(event, context):
-    """The actual lambda function entry point"""
-    events = parse(event, context)
-    metrics, logs = split(events)
-    if DD_FORWARD_LOG:
-        forward_logs(filter_logs(logs))
-    if DD_FORWARD_METRIC:
-        forward_metrics(metrics)
-
-
-if DD_FORWARD_METRIC:
-    # Datadog Lambda layer is required to forward metrics
-    lambda_handler = datadog_lambda_wrapper(datadog_forwarder)
-else:
-    lambda_handler = datadog_forwarder
-
-
-def forward_logs(logs):
-    """Forward logs to Datadog"""
-    scrubber = DatadogScrubber(SCRUBBING_RULE_CONFIGS)
-    if DD_USE_TCP:
-        batcher = DatadogBatcher(256 * 1000, 256 * 1000, 1)
-        cli = DatadogTCPClient(DD_URL, DD_PORT, DD_API_KEY, scrubber)
-    else:
-        batcher = DatadogBatcher(128 * 1000, 1 * 1000 * 1000, 25)
-        cli = DatadogHTTPClient(DD_URL, DD_API_KEY, scrubber)
-
-    with DatadogClient(cli) as client:
-        for batch in batcher.batch(logs):
-            try:
-                client.send(batch)
-            except Exception as e:
-                print("Unexpected exception: {}, batch: {}".format(str(e), batch))
-
-
-def parse(event, context):
-    """Parse Lambda input to normalized events"""
-    metadata = generate_metadata(context)
-    try:
-        # Route to the corresponding parser
-        event_type = parse_event_type(event)
-        if event_type == "s3":
-            events = s3_handler(event, context, metadata)
-        elif event_type == "awslogs":
-            events = awslogs_handler(event, context, metadata)
-        elif event_type == "events":
-            events = cwevent_handler(event, metadata)
-        elif event_type == "sns":
-            events = sns_handler(event, metadata)
-        elif event_type == "kinesis":
-            events = kinesis_awslogs_handler(event, context, metadata)
-    except Exception as e:
-        # Logs through the socket the error
-        err_message = "Error parsing the object. Exception: {} for event {}".format(
-            str(e), event
-        )
-        events = [err_message]
-    return normalize_events(events, metadata)
-
-
-def generate_metadata(context):
-    metadata = {
-        "ddsourcecategory": "aws",
-        "aws": {
-            "function_version": context.function_version,
-            "invoked_function_arn": context.invoked_function_arn,
-        },
-    }
-    # Add custom tags here by adding new value with the following format "key1:value1, key2:value2"  - might be subject to modifications
-    dd_custom_tags_data = {
-        "forwardername": context.function_name.lower(),
-        "memorysize": context.memory_limit_in_mb,
-        "forwarder_version": DD_FORWARDER_VERSION,
-    }
-    metadata[DD_CUSTOM_TAGS] = ",".join(
-        filter(
-            None,
-            [
-                DD_TAGS,
-                ",".join(
-                    ["{}:{}".format(k, v) for k, v in dd_custom_tags_data.iteritems()]
-                ),
-            ],
-        )
-    )
-
-    return metadata
-
-
-def extract_metric(event):
-    """Extract metric from an event if possible"""
-    try:
-        metric = json.loads(event['message'])
-        required_attrs = {'m', 'v', 'e', 't'}
-        if all(attr in metric for attr in required_attrs):
-            return metric
-        else:
-            return None
-    except Exception:
-        return None
-
-
-def split(events):
-    """Split events to metrics and logs"""
-    metrics, logs = [], []
-    for event in events:
-        metric = extract_metric(event)
-        if metric:
-            metrics.append(metric)
-        else:
-            logs.append(json.dumps(event))
-    return metrics, logs
-
-
-# should only be called when INCLUDE_AT_MATCH and/or EXCLUDE_AT_MATCH exist
-def filter_logs(logs):
-    """
-    Applies log filtering rules.
-    If no filtering rules exist, return all the logs.
-    """
-    if INCLUDE_AT_MATCH is None and EXCLUDE_AT_MATCH is None:
-        # convert to strings
-        return logs 
-    # Add logs that should be sent to logs_to_send
-    logs_to_send = []
-    # Test each log for exclusion and inclusion, if the criteria exist
-    for log in logs:
-        try:
-            if EXCLUDE_AT_MATCH is not None:
-                # if an exclude match is found, do not add log to logs_to_send
-                if re.search(exclude_regex, log):
-                    continue
-            if INCLUDE_AT_MATCH is not None:
-                # if no include match is found, do not add log to logs_to_send 
-                if not re.search(include_regex, log):
-                    continue
-            logs_to_send.append(log)
-        except ScrubbingException:
-            raise Exception("could not filter the payload")
-    return logs_to_send
-
-
-def forward_metrics(metrics):
-    """
-    Forward custom metrics submitted via logs to Datadog in a background thread
-    using `lambda_stats` that is provided by the Datadog Python Lambda Layer.
-    """
-    for metric in metrics:
-        try:
-            lambda_stats.distribution(
-                metric['m'],
-                metric['v'],
-                timestamp=metric['e'],
-                tags=metric['t']
-            )
-        except Exception as e:
-            print("Unexpected exception: {}, metric: {}".format(str(e), metric))
-
-
-# Utility functions
-
-
-def normalize_events(events, metadata):
-    normalized = []
-    for event in events:
-        if isinstance(event, dict):
-            normalized.append(merge_dicts(event, metadata))
-        elif isinstance(event, str):
-            normalized.append(merge_dicts({"message": event}, metadata))
-        else:
-            # drop this log
-            continue
-    return normalized
-
-
-def parse_event_type(event):
-    if "Records" in event and len(event["Records"]) > 0:
-        if "s3" in event["Records"][0]:
-            return "s3"
-        elif "Sns" in event["Records"][0]:
-            return "sns"
-        elif "kinesis" in event["Records"][0]:
-            return "kinesis"
-
-    elif "awslogs" in event:
-        return "awslogs"
-
-    elif "detail" in event:
-        return "events"
-    raise Exception("Event type not supported (see #Event supported section)")
-
-
-# Handle S3 events
-def s3_handler(event, context, metadata):
-    s3 = boto3.client("s3")
-
-    # Get the object from the event and show its content type
-    bucket = event["Records"][0]["s3"]["bucket"]["name"]
-    key = urllib.unquote_plus(event["Records"][0]["s3"]["object"]["key"]).decode("utf8")
-
-    source = parse_event_source(event, key)
-    metadata[DD_SOURCE] = source
-    ##default service to source value
-    metadata[DD_SERVICE] = source
-    ##Get the ARN of the service and set it as the hostname
-    hostname = parse_service_arn(source, key, bucket, context)
-    if hostname:
-        metadata[DD_HOST] = hostname
-
-    # Extract the S3 object
-    response = s3.get_object(Bucket=bucket, Key=key)
-    body = response["Body"]
-    data = body.read()
-
-    # If the name has a .gz extension, then decompress the data
-    if key[-3:] == ".gz":
-        with gzip.GzipFile(fileobj=BytesIO(data)) as decompress_stream:
-            # Reading line by line avoid a bug where gzip would take a very long time (>5min) for
-            # file around 60MB gzipped
-            data = "".join(BufferedReader(decompress_stream))
-
-    if is_cloudtrail(str(key)):
-        cloud_trail = json.loads(data)
-        for event in cloud_trail["Records"]:
-            # Create structured object and send it
-            structured_line = merge_dicts(
-                event, {"aws": {"s3": {"bucket": bucket, "key": key}}}
-            )
-            yield structured_line
-    else:
-        # Check if using multiline log regex pattern
-        # and determine whether line or pattern separated logs
-        if DD_MULTILINE_LOG_REGEX_PATTERN and multiline_regex_start_pattern.match(data):
-            split_data = multiline_regex.split(data)
-        else:
-            split_data = data.splitlines()
-
-        # Send lines to Datadog
-        for line in split_data:
-            # Create structured object and send it
-            structured_line = {
-                "aws": {"s3": {"bucket": bucket, "key": key}},
-                "message": line,
-            }
-            yield structured_line
-
-
-# Handle CloudWatch logs from Kinesis
-def kinesis_awslogs_handler(event, context, metadata):
-    def reformat_record(record):
-        return {
-            "awslogs": {
-                "data": record["kinesis"]["data"]
-            }
-        }
-        
-    return itertools.chain.from_iterable(awslogs_handler(reformat_record(r), context, metadata) for r in event["Records"])
-
-
-# Handle CloudWatch logs
-def awslogs_handler(event, context, metadata):
-    # Get logs
-    with gzip.GzipFile(
-        fileobj=BytesIO(base64.b64decode(event["awslogs"]["data"]))
-    ) as decompress_stream:
-        # Reading line by line avoid a bug where gzip would take a very long
-        # time (>5min) for file around 60MB gzipped
-        data = "".join(BufferedReader(decompress_stream))
-    logs = json.loads(str(data))
-
-    # Set the source on the logs
-    source = logs.get("logGroup", "cloudwatch")
-    metadata[DD_SOURCE] = parse_event_source(event, source)
-
-    # Default service to source value
-    metadata[DD_SERVICE] = metadata[DD_SOURCE]
-
-    # Build aws attributes
-    aws_attributes = {
-        "aws": {
-            "awslogs": {
-                "logGroup": logs["logGroup"],
-                "logStream": logs["logStream"],
-                "owner": logs["owner"],
-            }
-        }
-    }
-
-    # Set host as log group where cloudwatch is source
-    if metadata[DD_SOURCE] == "cloudwatch":
-        metadata[DD_HOST] = aws_attributes["aws"]["awslogs"]["logGroup"]
-
-    # When parsing rds logs, use the cloudwatch log group name to derive the
-    # rds instance name, and add the log name of the stream ingested
-    if metadata[DD_SOURCE] == "rds":
-        match = rds_regex.match(logs["logGroup"])
-        if match is not None:
-            metadata[DD_HOST] = match.group("host")
-            metadata[DD_CUSTOM_TAGS] = (
-                    metadata[DD_CUSTOM_TAGS] + ",logname:" + match.group("name")
-            )
-            # We can intuit the sourcecategory in some cases
-            if match.group("name") == "postgresql":
-                metadata[DD_CUSTOM_TAGS] + ",sourcecategory:" + match.group("name")
-
-    # For Lambda logs we want to extract the function name,
-    # then rebuild the arn of the monitored lambda using that name.
-    # Start by splitting the log group to get the function name
-    if metadata[DD_SOURCE] == "lambda":
-        log_group_parts = logs["logGroup"].split("/lambda/")
-        if len(log_group_parts) > 1:
-            function_name = log_group_parts[1].lower()
-            # Split the arn of the forwarder to extract the prefix
-            arn_parts = context.invoked_function_arn.split("function:")
-            if len(arn_parts) > 0:
-                arn_prefix = arn_parts[0]
-                # Rebuild the arn by replacing the function name
-                arn = arn_prefix + "function:" + function_name
-                # Add the arn as a log attribute
-                arn_attributes = {"lambda": {"arn": arn}}
-                aws_attributes = merge_dicts(aws_attributes, arn_attributes)
-                # Add the function name as tag
-                metadata[DD_CUSTOM_TAGS] += ",functionname:" + function_name
-                # Set the arn as the hostname
-                metadata[DD_HOST] = arn
-
-    # Create and send structured logs to Datadog
-    for log in logs["logEvents"]:
-        yield merge_dicts(log, aws_attributes)
-
-
-# Handle Cloudwatch Events
-def cwevent_handler(event, metadata):
-
-    data = event
-
-    # Set the source on the log
-    source = data.get("source", "cloudwatch")
-    service = source.split(".")
-    if len(service) > 1:
-        metadata[DD_SOURCE] = service[1]
-    else:
-        metadata[DD_SOURCE] = "cloudwatch"
-    ##default service to source value
-    metadata[DD_SERVICE] = metadata[DD_SOURCE]
-
-    yield data
-
-
-# Handle Sns events
-def sns_handler(event, metadata):
-
-    data = event
-    # Set the source on the log
-    metadata[DD_SOURCE] = parse_event_source(event, "sns")
-
-    for ev in data["Records"]:
-        # Create structured object and send it
-        structured_line = ev
-        yield structured_line
-
-
-def merge_dicts(a, b, path=None):
-    if path is None:
-        path = []
-    for key in b:
-        if key in a:
-            if isinstance(a[key], dict) and isinstance(b[key], dict):
-                merge_dicts(a[key], b[key], path + [str(key)])
-            elif a[key] == b[key]:
-                pass  # same leaf value
-            else:
-                raise Exception(
-                    "Conflict while merging metadatas and the log entry at %s"
-                    % ".".join(path + [str(key)])
-                )
-        else:
-            a[key] = b[key]
-    return a
-
-
-cloudtrail_regex = re.compile(
-    "\d+_CloudTrail_\w{2}-\w{4,9}-\d_\d{8}T\d{4}Z.+.json.gz$", re.I
-)
-
-
-def is_cloudtrail(key):
-    match = cloudtrail_regex.search(key)
-    return bool(match)
-
-
-def parse_event_source(event, key):
-    if "elasticloadbalancing" in key:
-        return "elb"
-    for source in [
-        "lambda",
-        "redshift",
-        "cloudfront",
-        "kinesis",
-        "mariadb",
-        "mysql",
-        "apigateway",
-        "route53",
-        "vpc",
-        "rds",
-        "sns",
-        "waf",
-        "docdb",
-        "fargate"
-    ]:
-        if source in key:
-            return source
-    if "API-Gateway" in key or "ApiGateway" in key:
-        return "apigateway"
-    if is_cloudtrail(str(key)) or ('logGroup' in event and event['logGroup'] == 'CloudTrail'):
-        return "cloudtrail"
-    if "awslogs" in event:
-        return "cloudwatch"
-    if "Records" in event and len(event["Records"]) > 0:
-        if "s3" in event["Records"][0]:
-            return "s3"
-
-    return "aws"
-
-
-def parse_service_arn(source, key, bucket, context):
-    if source == "elb":
-        # For ELB logs we parse the filename to extract parameters in order to rebuild the ARN
-        # 1. We extract the region from the filename
-        # 2. We extract the loadbalancer name and replace the "." by "/" to match the ARN format
-        # 3. We extract the id of the loadbalancer
-        # 4. We build the arn
-        idsplit = key.split("/")
-        # If there is a prefix on the S3 bucket, idsplit[1] will be "AWSLogs"
-        # Remove the prefix before splitting they key
-        if len(idsplit) > 1 and idsplit[1] == "AWSLogs":
-            idsplit = idsplit[1:]
-            keysplit = "/".join(idsplit).split("_")
-        # If no prefix, split the key
-        else:
-            keysplit = key.split("_")        
-        if len(keysplit) > 3:
-            region = keysplit[2].lower()
-            name = keysplit[3]
-            elbname = name.replace(".", "/")
-            if len(idsplit) > 1:
-                idvalue = idsplit[1]
-                return "arn:aws:elasticloadbalancing:{}:{}:loadbalancer/{}".format(
-                    region, idvalue, elbname
-                )
-    if source == "s3":
-        # For S3 access logs we use the bucket name to rebuild the arn
-        if bucket:
-            return "arn:aws:s3:::{}".format(bucket)
-    if source == "cloudfront":
-        # For Cloudfront logs we need to get the account and distribution id from the lambda arn and the filename
-        # 1. We extract the cloudfront id  from the filename
-        # 2. We extract the AWS account id from the lambda arn
-        # 3. We build the arn
-        namesplit = key.split("/")
-        if len(namesplit) > 0:
-            filename = namesplit[len(namesplit) - 1]
-            # (distribution-ID.YYYY-MM-DD-HH.unique-ID.gz)
-            filenamesplit = filename.split(".")
-            if len(filenamesplit) > 3:
-                distributionID = filenamesplit[len(filenamesplit) - 4].lower()
-                arn = context.invoked_function_arn
-                arnsplit = arn.split(":")
-                if len(arnsplit) == 7:
-                    awsaccountID = arnsplit[4].lower()
-                    return "arn:aws:cloudfront::{}:distribution/{}".format(
-                        awsaccountID, distributionID
-                    )
-    if source == "redshift":
-        # For redshift logs we leverage the filename to extract the relevant information
-        # 1. We extract the region from the filename
-        # 2. We extract the account-id from the filename
-        # 3. We extract the name of the cluster
-        # 4. We build the arn: arn:aws:redshift:region:account-id:cluster:cluster-name
-        namesplit = key.split("/")
-        if len(namesplit) == 8:
-            region = namesplit[3].lower()
-            accountID = namesplit[1].lower()
-            filename = namesplit[7]
-            filesplit = filename.split("_")
-            if len(filesplit) == 6:
-                clustername = filesplit[3]
-                return "arn:aws:redshift:{}:{}:cluster:{}:".format(
-                    region, accountID, clustername
-                )
-    return
\ No newline at end of file
diff --git a/logshipping.tf b/logshipping.tf
index 5fdbf5a..cffc7e9 100644
--- a/logshipping.tf
+++ b/logshipping.tf
@@ -75,15 +75,15 @@ resource "aws_iam_role_policy_attachment" "datadog-logshipping-lambda-attach3" {
 }
 
 resource "aws_lambda_function" "dd-log" {
-  filename      = "${path.module}/files/dd_log_lambda.zip"
+  filename      = "${path.module}/files/aws-dd-forwarder-3.5.0.zip"
   function_name = "${local.stack_prefix}DatadogLambdaFunction"
   role          = aws_iam_role.dd-log-lambda.arn
   handler       = "lambda_function.lambda_handler"
   description   = "This lambda function will export logs to our orgs Datadog events"
 
-  source_code_hash = filebase64sha256("${path.module}/files/dd_log_lambda.zip")
+  source_code_hash = filebase64sha256("${path.module}/files/aws-dd-forwarder-3.5.0.zip")
 
-  runtime     = "python2.7"
+  runtime     = "python3.7"
   memory_size = "1024"
   timeout     = "120"