If you are using Cloudflare, it can be helpful to configure Cloudflare to push request logs to S3. Otherwise, the Cloudflare dashboard provides only a limited view into your data (only 72 hours at a time and sampled data instead of full logs).

Once the Cloudflare request logs are in S3, they can be queried using Athena. This blog post even provides a nice CREATE TABLE command to set up the table in Athena.

However, there is a problem. When performing a query in Athena, it might have to scan all of the logs in S3, even if you try to limit the query. This can be slow and costly, as Athena queries are charged per byte scanned.

The only way to really limit the amount of data scanned is to partition the data.

This post assumes you have already set up Cloudflare to push logs to an S3 bucket, configured a database in Athena to access it, and then realized those logs will grow forever, along with your query times.

(If you just want the "how to" without the exposition, jump down to "Setting Up Partitions for Cloudflare Logs".)

Partitioning

Most commonly, you will want to look at logs from a specific time period, so it makes sense to partition the logs by date.

Most of the partitioning documentation suggests the files (or in this case, S3 objects) include a column=value key pair in the name. The column can then be used as a partition.

Unfortunately, Cloudflare does not allow customizing the format of the file names it produces.

Fortunately, the file names do include date/time information. The logs are grouped by date and time range:

s3://mah_s3_bucket/20210812/20210812T223000Z_20210812T224000Z_9af500e2.log.gz

So all we need to do is grab that date "folder" name and that's our partition! Easy, right?

No, wrong. This is AWS. Nothing is easy.

Use a Recurring Job?

Several of the AWS documentation pages suggest using ALTER TABLE to ADD PARTITIONs.

Something like this:

ALTER TABLE cloudflare_logs ADD
  PARTITION (dt = '2021-08-12') LOCATION 's3://mah_log_bucket/20210812/';

But since the logs will grow every day, we'll need to add new a new partition every 24 hours. It is not possible to "predefine" the partitions.

This requires setting up a recurring job... somewhere... to periodically define the new partitions. So now we have to pull in another AWS service to make S3 and Athena work nicely?! No thanks!

Partition Projection

At the bottom of the page about partitions, there is a paragraph about "partition projection" that sounds promising:

To avoid having to manage partitions, you can use partition projection. Partition projection is an option for highly partitioned tables whose structure is known in advance.

Yes, this is what we want! But how does it work?

Essentially like this:

We must define a column, its type, start and end values, and the interval between those values. Athena will then be able to extrapolate all the possible values.

Then we define a pattern to pull the value out of each S3 object name. This allows Athena to figure out which objects (logs) are associated with which partition value.

For example, the partition 20210812 will be associated with s3://mah_s3_bucket/20210812/20210812T223000Z_20210812T224000Z_9af500e2.log.gz

Once that's all done, we can query based on the partition as if it were a column, like:

SELECT * FROM cloudflare_logs
WHERE log_date >= '20210812'
  AND log_date < '20210901';

Setting Up Partitions for Cloudflare Logs

Here are the steps that must be taken to set up the partitions:

1. Add the partition "column" when creating the table
2. Set several properties on the table to define the projection
3. Set the partition pattern to match against object names
4. Enable projection

(Actually, 2-4 are all the same: set (totally unvalidated) key-value properties on the table.)

Fortunately, all of this can be accomplished with one giant Athena command:

CREATE EXTERNAL TABLE `YOUR_TABLE_NAME`(
  `botscore` int,
  `botscoresrc` string,
  `cachecachestatus` string,
  `cacheresponsebytes` int,
  `cacheresponsestatus` int,
  `clientasn` int,
  `clientcountry` string,
  `clientdevicetype` string,
  `clientip` string,
  `clientipclass` string,
  `clientrequestbytes` int,
  `clientrequesthost` string,
  `clientrequestmethod` string,
  `clientrequestpath` string,
  `clientrequestprotocol` string,
  `clientrequestreferer` string,
  `clientrequesturi` string,
  `clientrequestuseragent` string,
  `clientsslcipher` string,
  `clientsslprotocol` string,
  `clientsrcport` int,
  `edgecolocode` string,
  `edgecoloid` int,
  `edgeendtimestamp` string,
  `edgepathingop` string,
  `edgepathingsrc` string,
  `edgepathingstatus` string,
  `edgeratelimitaction` string,
  `edgeratelimitid` int,
  `edgerequesthost` string,
  `edgeresponsebytes` int,
  `edgeresponsecontenttype` string,
  `edgeresponsestatus` int,
  `edgeserverip` string,
  `edgestarttimestamp` string,
  `firewallmatchesactions` array<string>,
  `firewallmatchesruleids` array<string>,
  `firewallmatchessources` array<string>,
  `originip` string,
  `originresponsestatus` int,
  `originresponsetime` int,
  `originsslprotocol` string,
  `rayid` string,
  `wafaction` string,
  `wafflags` string,
  `wafmatchedvar` string,
  `wafprofile` string,
  `wafruleid` string,
  `wafrulemessage` string,
  `workersubrequest` boolean,
  `zoneid` bigint)
PARTITIONED BY (
  `YOUR_COLUMN_NAME` string)
ROW FORMAT SERDE
  'org.openx.data.jsonserde.JsonSerDe'
STORED AS INPUTFORMAT
  'org.apache.hadoop.mapred.TextInputFormat'
OUTPUTFORMAT
  'org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat'
LOCATION
  's3://YOUR_BUCKET_NAME/'
TBLPROPERTIES (
  'projection.enabled'='TRUE',
  'projection.YOUR_COLUMN_NAME.format'='yyyyMMdd',
  'projection.YOUR_COLUMN_NAME.interval'='1',
  'projection.YOUR_COLUMN_NAME.interval.unit'='DAYS',
  'projection.YOUR_COLUMN_NAME.range'='YOUR_START_DATE,NOW',
  'projection.YOUR_COLUMN_NAME.type'='date', 

 'storage.location.template'='s3://YOUR_BUCKET_NAME/${YOUR_COLUMN_NAME}/'
) 

These are the pieces related to partitioning:

PARTITIONED BY (
  `YOUR_COLUMN_NAME` string)

This tells Athena to set up a column for the partition.

(The type of the partition column must be string, even though the projection type must be date. Does this make any sense? NO.)

Then the table properties.

Turn on projection:

'projection.enabled'='TRUE'

Set the column type to date:

'projection.YOUR_COLUMN_NAME.type'='date'

This is so Athena knows how to interpolate values.

Define the date format to match:

'projection.YOUR_COLUMN_NAME.format'='yyyyMMdd'

Set the interval for the values to one day:

'projection.YOUR_COLUMN_NAME.interval'='1',
'projection.YOUR_COLUMN_NAME.interval.unit'='DAYS'

Set the range for the values:

'projection.YOUR_COLUMN_NAME.range'='YOUR_START_DATE,NOW'

NOW is a special value so the end of the range will always be the current day.

This sets a template to extract the date string from the object name, using the date template defined above, and setting the value in the column name specified for the projection:

'storage.location.template'='s3://YOUR_BUCKET_NAME/${YOUR_COLUMN_NAME}/'

If you are unfamiliar with Athena, it's good to know that deleting/creating tables is low impact. If the table is already created, it is not a big deal to delete it and start over.

Here are the important bits above that you will need to change:

• YOUR_TABLE_NAME is whatever you want to name the table. Something like cloudflare_logs would probably make sense.
• YOUR_COLUMN_NAME is whatever you want to name the projection "column". Could be dt like in the AWS docs, or log_date or whatever you want.
• YOUR_BUCKET_NAME is the name of the S3 bucket.
• YOUR START_DATE is the date of the first log. Something like 20210101.

The table should now indicate it is partitioned:

And the partition should show up as a column:

Using Date Partitions

To test if partitions are working as expected, a quick query like this will work:

SELECT DISTINCT(YOUR_COLUMN_NAME)
FROM "YOUR_TABLE_NAME"
LIMIT 10;

The expected output is several dates for which there are logs.

Once that is confirmed, the partition column can be used like any other column. Since the values is a basic ISO date format, comparison operators can be safely used even though the column is really just a string.

For a single day:

SELECT * FROM YOUR_TABLE
WHERE YOUR_COLUMN_NAME = "20200101";

For an inclusive range:

SELECT * FROM YOUR_TABLE
WHERE YOUR_COLUMN_NAME >= "20200101"
  AND YOUR_COLUMN_NAME <= "20210101";

And now your queries can be faster and cheaper!