How to Architect Your Cribl Stream to Stream Data Flows

Brandon McCombs
Written by Brandon McCombs

March 10, 2021

We are increasingly asked if a Cribl Stream instance can be used to send to another Stream instance. The answer is a definite yes! While there are various reasons for wanting to send data from one Stream instance to another, let’s walk through just one example: collecting data in one AWS region using Stream, while sending (using compression to minimize cost) to another instance in a different region.

We will look at a comparison of various supported protocols for accomplishing this, as well as provide our recommendation. We will also discuss data flow architecture options that may be of interest for multiple Stream instances.

Recommended protocol

As with receiving data from any other source, the question of whether the data can be received at a destination is simply a matter of finding a common protocol that both the source and the destination support. In this scenario, Stream is both the sender and receiver therefore we have to find a protocol that Stream can use as a method for both receiving data and sending it. The following protocols currently satisfy this criterion:

  • Syslog
  • Elastic API
  • Splunk HEC
  • Splunk TCP

While any of these five protocols are acceptable for sending data from one Stream instance to another, one protocol stands out as the best option using the features below as criteria for ranking purposes.

 Name   TLS   Compression   Load
  Queueing (PQ)
  Cribl native   Lightweight
 TCP JSON   Yes   Yes   No   Yes   Yes   Yes
 syslog   Yes   No   No   Yes   No   Yes
 Elastic API   Yes   Yes   No   Yes   No   No
 Splunk HEC   Yes   Yes   No   Yes   No   No
 Splunk LB   Yes   No   Yes   Yes   No   Yes

If you need to transfer from one LogStream instance to another within AWS, then TCP JSON is preferred over Splunk HEC and Elastic because it is lighter-weight and a native Cribl protocol, while still saving on inter-region data transfer costs via compression. And you can utilize AWS’s load balancing capabilities until load balancing is natively supported. (That feature is on the roadmap!) Without an external load balancer, the TCP JSON destination type will create a TCP socket, and remain bound to that host and port until the connection is broken. So you’ll want a load balancer between the source and the destination LogStream instances, to properly distribute across the destination’s LogStream workers.

What about the other options?

Syslog is lightweight, but Stream does not currently support compression or load balancing with syslog. Of the six features used for comparison in the table, syslog provides three of them.

Elastic and Splunk HEC are essentially equal to each other in specific functionality using the six feature criteria and both rank equal to syslog by providing three features.

Splunk LB is the only protocol that supports native load balancing, but without compression, your interregional AWS costs may be prohibitive. It surpasses syslog, Elastic API and Splunk HEC as far as offering the most features with four.

So what’s the takeaway here? TCP JSON is a Cribl protocol that supports TLS, PQ, and compression, but without the overhead of HTTP or the Splunk TCP protocol. So it stands out as the best choice, despite lacking a load-balancing capability for now.

Did I forget some protocols?

Astute readers may ask “But what about Amazon S3, Amazon Kinesis, Apache Kafka, or Azure Event Hubs?” Stream also supports those as both Sources and Destinations, and you are welcome to use those, if one or more of them suit your needs better than TCP JSON or another protocol mentioned above.

However, the protocols highlighted above are those for which Stream supports direct host to host communications between the Stream Worker Nodes. These others involve an intermediary. This intermediary will cause one or more of the following: latency, extra (hard and/or soft) costs, complexity, etc. 

If you are considering the possibility of a Kafka variant as an acceptable intermediary because of queuing needs, keep in mind that all five protocols compared above provide dynamic PQ (i.e., PQ is only used when necessary and the need is automatically detected). By using PQ only when needed, you can save some latency by not using Kafka (even though Kafka is highly optimized for low latency) and also save the added cost of data stored on disk.

Other considerations

When transmitting observability data from one Stream instance to another, a few additional considerations come to mind as one digs deeper into planning: When using Worker Groups, does it matter whether each Worker Group is controlled by its own Master Node? What about licensing costs that may be incurred? Let’s address those concerns.


I’ll tackle the latter question first because it’s a simpler discussion. Cribl Stream tracks data on ingest only for purposes of licensing. You can see a 90-day historical trend graph in Monitoring > Licensing that reflects this tracking. Both the source and destination Stream instances will reflect receiving inbound data on the Monitoring > Licensing page. You can ingest data with a standalone or distributed Stream instance to use for sending to one or more additional Stream instances, without incurring additional costs within the Stream environment.

Worker Group Architecture

When transmitting observability data from one Stream instance to another, the configuration is really simple when using standalone instances, but questions can arise when using a distributed environment. There can be many variations within user environments. but those variations are just some combination of the two scenarios shown here. 

For various reasons, users may need to have multiple Master Nodes managing one or more Worker Groups. The Worker Groups may be organized by region, data center, prod vs test, etc. In the diagram below, these groups are deployed by region, with each region having its own Master Node.

In other situations, an organization may be able to better leverage a single Master Node across multiple Worker Groups, to provide easier management and monitoring of the entire Stream environment. This data flow architecture looks like the following diagram:

With any combination of where the master exists, or how many masters there are, the result is the same. The master has no effect on how these Worker Groups process their inbound and outbound data, and this holds true whether the inbound data is from another Stream instance or from a 3rd party tool, so long as the common-protocol rule is followed. As with any data flow architecture, be aware of the TCP/UDP ports being configured to ensure the corresponding ports in your firewalls are open.


As you can see, Cribl Stream is quite versatile with how it can send and receive observability data. This versatility will only increase over time. To learn more about Stream to Stream operations, check out our video resourse.

As always, we recommend trying Cribl Stream for yourself through one of our sandbox courses. Additionally, you can download Stream and process up to 5TB/day for free.

Questions about our technology? We’d love to chat with you.

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