Serverless S3 Access Logs Analytics using BigQuery

Configuring a Serverless and full-managed system to analyze S3 access logs using BigQuery.

Multi-cloud Data Analytics

Do you use BigQuery? Are you interested in knowing how to integrate data from different cloud providers into BigQuery? In this blogpost, we will implement a serverless and fully managed system to make available S3 access logs into BigQuery to easily integrate them with other data sources and reporting systems. To achieve this we will see how to set up AWS S3 access logs delivery and configure Google Data Transfer Service in order to schedule fully managed S3 to Cloud Storage transfers.We will also use BigQuery external table to read data directly from Google Cloud Storage to access always to the most recent data without keeping reloading data into BigQuery. Finally, we will leverage persistent UDFs to parse the logs into tabular format on the fly.

Set up S3 access logging

AWS allows you to export S3 access logs, which can be used to monitor user access and build business metrics. Although, you can export logs for a specific bucket into itself, this might cause extra logs to get generated. In our scenario we use a different bucket to collect and store the access logs of many other buckets. Also, S3 access logs can be exported only to a bucket in the same region for the monitored one. So we will need to create a different bucket for each region where we have buckets that we want to monitor. In this scenario, we will work in the “eu-west-1” region. Let’s create a bucket to collect and store the access logs.

aws s3api create-bucket --bucket MY_BUCKET_EU-WEST-1 --region eu-west-1

Now we need to set up S3 access logs delivery. First we grant the AWS LogDelivery group the relevant permissions to our destination bucket:

aws s3api put-bucket-acl --bucket MY_BUCKET_EU-WEST-1 --grand-write URI=http://acs.amazonaws.com/groups/s3/LogDelivery --grant-read-acp URI=http://acs.amazonaws.com/groups/s3/LogDelivery

Then we define the logging configuration locally in a JSON file:

{
  "LoggingEnabled": {
    "TargetBucket": "MY_BUCKET_EU-WEST-1",
    "TargetPrefix": "BUCKET_TO_LOG/",
  }
}

TargetBucket is the bucket where the logs will be delivered. You can provide a prefix for the logs using TargetPrefix (optional), we use it to separate the logs of different buckets but you could use a different logic. Now we need to enable logging:

aws s3api put-bucket-logging --bucket BUCKET_TO_LOG --bucket-logging-status file://logging.json

It’s worthy to remember that logs are delivered on a best effort basis, usually most log records are delivered within a few hours of the time they are recorded, but can be more frequently.

Transfer logs to Google cloud Platform

In our scenario, we will transfer the data first into Cloud Storage using the Data Transfer service and then using BigQuery external table to run queries on them. Another possible solution would be to use S3 to BigQuery data transfer service to directly transfer the data into BigQuery. The Data Transfer service is fully managed, so you just need to configure it and you won’t need to provision and monitor any infrastructure. Transfers are scheduled to run every 24 hours, if you need a different interval you will have to implement the data transfer yourself instead of using this service. Before setting up the transfer we need a destination bucket on Cloud Storage. Let’s create a multi-regional bucket in Europe.

gsutil mb -l EU gs://MY_AWS_S3_ACCESS_LOGS_MIRROR/

To set up the transfer service, log into your cloud console to find Data Transfer under the Storage section. Now click on “Create a transfer job” and follow the steps below: As a source we select “Amazon S3 bucket and include the bucket name we want to read from, in our example “MY-S3-ACCESS-LOGS-EU-WEST-1”. We need to provide AWS authentication Access key ID and Secret access key that have the permission to read the bucket. As the destination, we put the bucket we just created MY_AWS_S3_ACCESS_LOGS_MIRROR and leave all the other options un-ticked. We select to run daily at a specific time. Click on Create.

For more detailed information refer to the official documentation.

We still need to be able to access the data in BigQuery to do our analytics and build reporting dashboards with Data Studio. Unfortunately, AWS S3 log entries are not in JSON or CSV-like format but they use their own. For this reason, BigQuery cannot automatically load it into a table with the desired schema. To have the logs parsed as we desire we will use a persistent UDF but first we need to make the log entries “available” to BigQuery. To do so, we define an external table that reads data from our mirror bucket in Cloud Storage and we have to configure it as the files use newline as field separator. As a result, we want a table where each row contains an entire log entry, we choose the newline character as field separator because we do not expect to have any of them in our log entries. This will result in a table with a single column. To create our source table we can use the cloud console: Select the dataset you want to create your table in and click create table. In the table creation menu, select as source “Google Cloud Storage”, CSV as format, and add the prefix of the data that you want to be the sources of the table (remember that you can use wildcard to say to BigQuery to select all the files within the prefix). Choose your table name (in this example we will use “s3_raw_logs_external”) and because we do not want to actually load the data, we need to select “External table” in “Table type”. In the schema section add a single column called “text” of type STRING. Click on “Advanced options” and set the field delimiter to be “Custom” and insert s character that should never be present (e.g. “§"). You can leave the other options with their default values and click “create”.

Now we have a table into BigQuery where the source data is in Cloud Storage, this means that every query reading from this table will always acces all the data present in the bucket at that time. Note that this setup might start having degraded performances for very large logs datasets.

Transform raw logs into tabular format

As we said above , AWS S3 log entries are not in JSON or CSV-like format but they use their own format, so we need to parse them ourselves . We can easily do this directly in BigQuery using a persistent UDF. I wrote a Javascript UDF to parse most of the log entry parts into an object. Note that we use “\” instead of “" because we need to escape when defining the function.

You can copy and paste the code above into the BigQuery UI to create the s3_log_parser as a persistent UDF, just replace PROJECT_ID and DATASET_ID with valid values for your environment.

Now we have easy access to the data and the logic to transform it into tabular format, the following query will parse all the logs entry into the column defined in the UDF.

Let’s copy and paste this query into the BigQuery UI and run it. If you have already data into the Cloud Storage bucket you will see the logs parsed out. Now click on “Save view”, choose a destination dataset and a name for the view (e.g. s3_logs_parsed). In this way, we can use the view as a source for our analytics reporting dashboards instead of always writing the query itself. Now try the following query, it should show the same results of the previous one.

Select
  *
from `PROJECT_ID.DATASET_ID.s3_logs_parsed`

Now you have a serverless, fully-managed system to analyse your AWS S3 access logs into BigQuery using SQL and play with Google Datastudio to build and share reporting dashboards.

Principal Data Platform Engineer

My interests include data engineering at scale, geospatial analytics, machine learning, windsurf, and cooking.

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