Thursday, July 13, 2023

Buggy Behavior From The CloudWatch Logs API

Tip 26: “select” isn't broken.
The OS is probably not broken. And the database is probably just fine.

The Pragmatic Programmer

Words to live by — although I did once find a problem with Oracle's index management, which manifested in select. In my opinion, it's the maxim that defines a senior developer. Junior developers always look outside themselves whenever they run into a problem. Senior developers do everything they can to find where they've made a mistake. After all, there are a lot of people doing exactly the same things that you are; surely they would have found the bug first.

But sometimes, it really is a bug in the system. This post is a story of how I determined the bug wasn't in my code. It's part catharsis, part “here's what you should do if you think select is broken,” and part calling out the issue for posterity in case someone else trips over it.

In January of this year (2023), AWS made a change in the CloudWatch Logs API: you no longer needed to provide a sequence token when calling PutLogEvents. Sequence tokens were always, I think, a “leaky abstraction”: a part of the internal implementation of CloudWatch Logs that found its way into the API. To summarize: you had to retrieve a sequence token before you could write events. When you wrote a batch of events, you received a sequence token back, which you could use to write the next batch — unless another process was writing to the same stream, in which case your sequence token would become invalid. Which meant that you had to retrieve a new token, using an API that had a relatively low limit on number of requests per second. With enough concurrent writers, hilarity would ensue as each would retrieve a token, try to use it, and fail because some other writer got there first.

This change directly impacted my logging library, but initially I wasn't planning to adopt it: the library worked, the old behavior was supported, and to be honest, releasing the library involves a lot of toil. But then someone found a bug with my sequence-token retrieval code if the logging library happened to re-initialize itself (Log4J2 FTW!). I fixed the immediate bug, and released a patch, but then decided it was a sign from the universe that I should remove the sequence token entirely. Plus, it always feels good to delete code!

However, once I changed the code, one of my integration tests — the one that pits concurrent writers against each other — started failing. When it tried to verify the events that were written, it would often come up short.

Failing integration tests are nothing new to this library: CloudWatch is an “eventually consistent” system, and while “eventually” often means seconds, it sometimes takes longer for the service to become consistent. As a result, failing tests don't clean up after themselves, so that I can look at what happened. I could then use my log reader utility, and verify that all of the events were eventually available.

This time, I didn't get all of the events back. Of course, I assumed that my reader was incorrect, and dove back into the documentation for the API calls it was using. I experimented with different arguments to the GetLogEvents call, and also tried FilterLogEvents. But nothing I did had an effect: I was still missing log entries.

Next step was to dig into the logging library. I already had it fairly well instrumented: if you enable debug mode in the logging framework it will report everything that it does, including (optionally) every batch that it sends. Everything looked good, but there was a corner case that worried me: CloudWatch can reject events, and unlike Kinesis, it doesn't tell you which events it rejects. The appender pre-processed batches to remove invalid events, but didn't log failures (because there shouldn't be any). I changed that code, but no failures appeared.

At this point I didn't think the bug was in my appender, but there was always the possibility that I was doing something incorrectly that I didn't log. My next step for debugging the appender would have been to turn up the logging level within the AWS SDK, to get a detailed report of what it was doing. However, this can become a case of drowning in detail, so first I looked at alternative methods of verifying the events.

In addition to the GetLogEvents and FilterLogEvents API calls, CloudWatch provides a query API. This API requires you to submit a query and then loop until it's done, so I didn't have a utility program that used it. But it's what the Console uses in the Insights page.

So I wrote a simple query to retrieve events from the stream; I was actually more interested in the reported count, rather than looking at all 20,000 expected events. But Insights told me that there weren't any events in the stream.

At this point, I opened a ticket with AWS Support (fortunately, I have access to an account with a Business support contract, since “Basic” support no longer allows technical questions). I received an answer that “as designed,” Insights does not return any messages with timestamps prior to the log group's creation time. WTF?!?

The back-story is that my Log4J 1.x appender lazily creates the log group and stream when it first writes a batch of messages, because Log4J 1.x does not have an explicit initialization phase. This is definitely something to be aware of if you use my library with Log4J 1.x (and, overwhelmingly, that's what my download counts indicate people are doing). The solution is to pre-create your log groups, so I modified the test to do so. This is not an issue with Log4J 2.x and Logback, which do have an initialization phase.

But the test continued to fail. However, I could view the logs in Insights, and saw that it was able to retrieve all of the events (yet another approach, which also succeeded, is export to S3). And more interesting, it was the initial few log messages that weren't being retrieved by GetLogEvents. I also called the DescribeLogStreams API, and it returned a later “first event timestamp” than expected, by a few milliseconds.

This seemed worth another Support ticket: if I could retrieve all log events using one technique, and not using another, then clearly it was a problem with CloudWatch and not me.

And now comes the most important lesson from this entire adventure: do not assume that the AWS Support rep can see what you're describing, and more important, do not expect them to tell you that they can't.

I had created a log group/stream that demonstrated the problem, referenced it in the ticket. I also attached the S3 export, the list of events from my log reader, and the initial events from an Insights query. The support rep seemed to agree that there was a problem. Then for a week they wrote nightly updates about how they were trying to re-upload the events and did not see a problem. They seemed to be trying random things, so I recommended building my library and running the integration test — at this point, my assumption was that AWS accepted that there was a problem based on my example group/stream, and were simply trying to re-create at will.

It turned out this was an incorrect assumption, which I learned when the original rep went dark and a new rep picked up the case. I had independently created a program that triggered the issue, so that they wouldn't have to run the integration tests. The new rep claimed to have run the program, did not see the issue, and pointed out that AWS did not have permission to view the original log group and stream that I had identified in the ticket!

Seeing that the “web” interactions were not having any effect, I decided to try a chat session with a screenshare. I showed the rep the original group and stream, and how different retrieval methods returned different results. At the time it didn't seem to have much effect: he made sounds of acknowledgement, but was unable to assume the role I created to let him read events, and would go silent for minutes at a time while he was “try[ing] something.” All-in-all, not a great experience from my side.

But it did seem to have an effect: the next morning I got a message from someone who claimed to be on the CloudWatch team, saying that they were able to re-create the problem. And that's where it stands today. For my part, the updates to my library are on hold until I hear back: while this may be superstition on my part, it seems to work better if I provide a sequence token (I suspect it's actually the delay from retrieving the token).

And if you run into a problem with GetLogEvents not returning all of your events, feel free to reference case #12882575541 if you file a Support ticket.

Update, 2023-08-22

AWS has resolved the problem: "We investigated and found that there was an issue with the service which in rare situations can incorrectly set the "firstEventTimestamp" on a log-stream [...] Our team has now fixed the issue with GetLogEvents not returning some events due to incorrectly set "firstEventTimestamp"." Have verified that my integration tests run (repeatedly) without a failure. It took just under three months (05/23 to 08/18) to resolve the case, two months (06/15) from the point that someone took it seriously. For a company the size of AWS, and a service with the reach of CloudWatch Logs, that seems pretty fast; I've had issue drag on far longer with in-house IT teams.

Tuesday, January 3, 2023

Using EventBridge Pipes to route CloudWatch Logs events to Kinesis

Several years ago I wrote about connecting CloudWatch Logs into into a logging pipeline built on Kinesis Streams and Kinesis Firehose. To make that work, I wrote a Lambda.

AWS announced EventBridge Pipes at re:Invent 2022, as an easy way to connect messaging services. Connecting CloudWatch Logs to Kinesis seemed like a good way to kick the tires on this service. It still requires a custom Lambda, but that Lambda is now much simpler, because it doesn't need to concern itself with the quirks of PutRecords.

The Problem

When you subscribe a Kinesis stream to a log group, CloudWatch writes messages that look like this:


That's a GZipped and Base64-encoded JSON object. When decoded, unzipped, and pretty-printed, it looks like this:

  "messageType": "DATA_MESSAGE",
  "owner": "123456789012",
  "logGroup": "AppenderExample",
  "logStream": "Log4J1-Example-20221224-ithilien-351916",
  "subscriptionFilters": [
  "logEvents": [
      "id": "37284354508805523945825240107552419724039002310923255808",
      "timestamp": 1671888278929,
      "message": "2022-12-24 08:24:38,929 DEBUG [example-0] - value is 52\n"
      "id": "37284354508805523945825240107552419724039002310923255809",
      "timestamp": 1671888278929,
      "message": "2022-12-24 08:24:38,929 DEBUG [example-1] - value is 52\n"

As you can see, CloudWatch combines multiple log events into a single message. However, most destinations — such as Elasticsearch — want individual events, not a nested array. So you need something that pulls apart the original event. For example, the AWS-provided OpenSearch integration, which subscribes a Lambda to the log group, decomposes the messages, and writes them to the cluster. My previous implementation did a similar transformation, but wrote the log messages to a Kinesis stream.

EventBridge Pipes

EventBridge Pipes is modeled around a four-step process (image sourced from docs):

EventBridge Pipes data flow

For simple pipelines, EventBridge gives you a “no code” solution that eliminates the middle two steps: you define an output format using path references to fields in the input messages. Unfortunately, this isn't usable for CloudWatch Logs messages: it can't (currently) handle Gzipped data, and can't extract the elements in an array. So we need to add an “enrichment” step that performs this operation.


You can implement the enrichment step in multiple ways, including an HTTP(S) API, but the easiest is a Lambda. Pipes invokes this Lambda with an event that contains one or more source records, and it returns returns zero or more result records.

The source event looks like this:

    "eventSource": "aws:kinesis",
    "eventVersion": "1.0",
    "eventID": "shardId-000000000000:49636526735590005452916707570422271019264002608211165186",
    "eventName": "aws:kinesis:record",
    "invokeIdentityArn": "arn:aws:iam::123456789012:role/CloudWatchTransformPipeline-PipeRole-us-east-1",
    "awsRegion": "us-east-1",
    "eventSourceARN": "arn:aws:kinesis:us-east-1:123456789012:stream/log-subscription",
    "kinesisSchemaVersion": "1.0",
    "partitionKey": "87362ce96c65eb1b3125bf0129b99bee",
    "sequenceNumber": "49636526735590005452916707570422271019264002608211165186",
    "data": "...",
    "approximateArrivalTimestamp": 1672490859.613

The first thing to call out is that this event is an array. EventBridge Pipes can batch messages together, so you must loop over the entire event and combine the results from each message (ie, a flat-map):

def lambda_handler(event, context):
    result = []
    for message in event:
        result += transform_messages(message)
    return result

The data element is the GZipped and Base64-encoded message that I showed above. You process it with code that looks like this:

data = base64.b64decode(message['data'])
data = gzip.decompress(data)
payload = json.loads(data)

The last thing to be aware of is that there are two types of messages that you'll get from a CloudWatch Logs subscription, identified by the messageType field. When the subscription first starts, CloudWatch sends a control message to the stream to verify that it can write messages. After that, it sends data messages. Your Lambda should ignore everything but the latter.

if payload['messageType'] == 'DATA_MESSAGE':
    # extract log events
    return []


This post has shown you the basics of an EventBridge Pipes transform. You can find my complete example, which performs several transforms on the log events, here.

While I don't think that EventBridge Pipes quite lives up to the hype of Werner Vogels' keynote (and that it has nothing in common with EventBridge other than a name), it does allow you to focus on your logic rather than the quirks of your source and destination. As such, it's a useful addition to your data transformation toolbox.

Sunday, January 1, 2023

Does that i4i.metal instance really help your build times?

Here in Philadelphia, the last day of 2022 was rainy and foggy; a day to stay inside. That, combined with $100 in AWS credits that were due to expire with the new year, made me decide to add another entry to my series of posts evaluating build performance. This time, I'm comparing my desktop PC to the current AWS king of the hill, an i4i.metal instance.

If you don't make a habit of reading EC2 instance specs, the i4i.metal has 128 virtual CPUs (32 actual cores), 1 terabyte of RAM, 30 terabytes of locally-attached SSD storage spread over 8 drives, and 75,000 megabits of network bandwidth. It costs $10.982 per hour ($96,268.212 per year) on-demand in us-east-1. There are more expensive EC2 instance types; the p3dn.24xlarge costs $31.212 per hour, but has fewer vCPUs, less memory, and less disk.

To see if the local SSD offered a performance improvement over EBS, I configured a 1 terabyte gp3 drive as the machine's root volume, and formatted one of the attached SSDs as ext4. I created a separate user, whose home directory was on the SSD, so all of the reads and writes for the builds should have stayed there.

As a comparison, I have my ten-year-old desktop: an i7-3770k (not overclocked) with 32 GB of RAM and a 512 GB Samsung 860 Pro SSD. It cost around $750 to build (no graphics card). I also spun up an m5.large EC2 instance, for consistency with previous tests.

Both of the AWS instances were running Amazon Linux 2; my devbox runs Xubuntu 20.04. All machines ran OpenJDK 1.8, although the specific builds varied (it's whatever the package manager installed). And I manually installed Maven 3.5.4 on all of the machines (it's what I'm running at home).

For a test workload, I built the AWS V2 Java SDK (commit 0f7779ef, corresponding to version 2.19.9-SNAPSHOT). This is a much bigger build than most people will ever deal with: almost 400 sub-projects, and over 100,000 Java files (including tests and generated code). I skipped tests, because it was clear that some of the tests involved timing delays; I wanted to see pure CPU/disk performance.

I ran three builds with each configuration. The first was a “clean” build, forcing Maven to download all dependencies. The second was with empty buffer cache (echo 3 > /proc/sys/vm/drop_caches), and the third, run immediately afterward, should have benefited from any files that were in the cache. As it turned out, the numbers were so close that it didn't make sense to report them separately; all numbers in the table below are from the “empty cache” run (ie, no downloads involved). As with previous tests, I show the output from the Linux time command; the “real” column is the elapsed time of the build, and is what developers care about.

  Real User Sys
My DevBox 27m 24.406s 80m 3.772s 1m 16.792s
m5.large 54m 40.495s 94m 1.207s 1m 46.047s
i4i.metal EBS 17m 49.500s 97m 30.694s 2m 19.089s
i4i.metal SSD 17m 44.165s 97m 6.427s 2m 11.365s

I have to be honest, I was surprised: I expected a small improvement in performance, but not 35%! My guess at the reason for the difference, based on the higher ”user” time, is that it's able to make more effective use of those 32 cores. The 55 MB CPU cache (versus 8MB on my PC) may have helped as well. I think the only way to summarize this test is with the words of Ferris Bueller:

It is so choice. If you have the means, I highly recommend picking one up.