Marshal is in beta. We have deployed it in a few places and are working to ensure it's stable and fast. It is not 100% battle tested yet, feedback is very welcome.
This project assumes you have some familiarity with Kafka. You should know what a topic is and what partitions are.
In Kafka, the unit of scalability is the partition. If you have a topic that is getting "too busy", you increase the partition count. Consumption of data from those busy topics requires consumers to be aware of these partitions and be able to coordinate their consumption across all of the consumers.
Traditional setups use Zookeeper or some other system for coordinating consumers. This works in many situations, but introduces a point of failure that isn't necessary. It is possible to completely perform consumer coordination using Kafka alone.
Additionally, getting consumer coordination correct is a rather taxing exercise in development and, frankly, shouldn't need to be done for every single project, company, etc. There should be an open source system that handles it for you.
Marshal is a library that you can drop into your Go programs and use it to coordinate the consumption of partitions across multiple processes, servers, etc. It is implemented in terms of Kafka itself: zero extra dependencies.
Marshal is designed for use in production environments where there are many topics, each topic having hundreds of partitions, with potentially thousands of consumers working in concert across the infrastructure to consume them. Marshal is designed for big environments with critical needs.
This module is designed to be extremely simple to use. The basic logical flow is that you create a Marshaler and then you use that to create as many Consumers as you need topics to consume. Logically, you want one Marshaler in your program, and you want a single Consumer per topic that you need to consume from.
Here's the simplest example (but see a more complicated example in the example directory):
package main
import "fmt"
import "github.com/zorkian/marshal/marshal"
func main() {
marshaler, _ := marshal.NewMarshaler(
"clientid", "groupid", []string{"127.0.0.1:9092"})
defer marshaler.Terminate()
consumer, _ := marshaler.NewConsumer(
[]string{"some-topic"}, marshal.NewConsumerOptions())
defer consumer.Terminate()
msgChan := consumer.ConsumeChannel()
for {
msg := <-msgChan
fmt.Printf("Consumed message: %s", msg.Value)
consumer.Commit(msg)
}
}If you were to hypothetically run this against a cluster that contained
a topic named some-topic that had 8 partitions, it would begin
claiming those partitions one by one until it had them all. If you
started up a second copy of the program, it would only claim the
partitions that are not already claimed. If the first one dies, the
second one will pick up the dropped partitions within a few minutes.
In essence, Marshal takes all of the effort of consumer coordination out of your software and puts it where it belongs: on Kafka.
Please read this section to get a handle on how Kafka performs coordination and the guarantees that it gives you. In particular, the failure scenarios might be interesting.
If you want the gory details about the protocol used internally, please see the PROTOCOL documentation. You don't need to read and understand it, though, but it might be useful.
In essence, Marshal uses a special topic within Kafka to coordinate the actions of many consumers anywhere in the infrastructure. As long as the consumers can connect to the Kafka cluster you want to coordinate, you can use Marshal. There is no language dependency either -- Marshal the algorithm could be implemented in any language and consumers could coordinate with each other.
We assume that you're familiar with the basics of Kafka -- notably that each partition is effectively a write-ahead log that records an ordered set of events, and that it's not possible (barring unclean leader elections) for two consumers to see different event orderings. Marshal takes advantage of that property to perform distributed coordination.
When a program using Marshal starts up, the first thing it does is read the logs in the coordinating topic. These logs contain certain events, such as: claim partition, heartbeat, and release partition to name a few.
Using these events Marshal can know not only what consumers exist, but what partitions they are currently working on and how far along they are. Using that information the local program can decide such things as "which partitions are unclaimed" and then take action to claim and begin consuming those partitions.
Coordination happens within "groups". When you create a Marshaler you
can specify the group that your consumer is part of. All claims are done
on a per-group basis, which means you can consume the same topic N times
-- as long as you have N groups. There is a one-to-one mapping between
"consumers that can claim a given partition" and "number of groups".
The "client ID" specified when you create a Marshaler is used to
identify a particular instance of a program. These should be unique per
instance of software, but they should be reasonably stable. At Dropbox
we use the name of the machine the software is running on, plus possibly
an instance ID if we run multiple copies on a single box.
The main engine of Marshal happens when you create a consumer and call
consumer.Consume(). This will possibly return a message from one
of the partitions you have claimed. You then do something with the
message... and consume the next one. You don't have to do anything else.
Behind the scenes, the act of consuming updates internal cursors and timers and will possibly generate heartbeat messages into the Marshal event log. These messages contain information about the last offset consumed, allowing other consumers (and monitoring systems) to know where you are within the partition. In case of failure, they can resume at the last point you heartbeated.
Presently, all consumption within Marshal is at least once. In case of most consumer failures, it is likely a block of messages (one heartbeat interval) will be reprocessed by the next consumer.
Kafka guarantees the ordering of messages committed to a partition, but does not guarantee any ordering across partitions. Marshal will give you messages from any partition it has claimed, so in essence, Marshal does not guarantee ordering. If you need message ordering, this library is not presently appropriate for you.
If you are having throughput problems you should increase the number of partitions you have available so that Marshal can have more in-flight messages.
This documents some of the failure modes and how Marshal handles them. Please let us know about more questions and we can analyze and write about them.
In the case where a consumer is too slow -- i.e. it is consuming more slowly from a partition than data is coming in -- Marshal will detect this and internally it will start failing its health checks. When this happens it will, after enough time has passed, decide that it is not able to sustain the load and will voluntarily surrender partitions.
This is useful as a load balancing mechanism if you happen to have one consumer that ends up with 8 claims while another has only a handful, the former can shed load and the latter will pick it up.
However, it is worth noting that in the unbalanced scenario, as long as the consumers are keeping up with the traffic they won't release partitions. It is perfectly valid right now for Marshal consumers to end up unbalanced -- as long as they're all pulling their weight.
If a consumer dies or shuts down in an expected (controlled) way, Marshal will attempt to commit release partition events into the log. If this happens successfully then other consumers will be able to pick up the partitions within seconds and begin consuming exactly where the last consumer left off.
No data is skipped or double-consumed in this mode and the downtime is extremely minimal.
If a consumer dies unexpectedly, things are slightly worse off. Assuming a hardware failure or other such issue (network split, etc), the partition's claim will start to become stale. From the perspective of the rest of the fleet, they will have to wait an appropriate interval (two heartbeats) until they can claim the partition.
Data might be double-consumed, but the maximum amount is one heartbeat's worth. Depending on the last time you heartbeated, at worst you will see that many messages be double-consumed. The downtime of consumption is also up to two heartbeat intervals at worst.
Since Kafka can only have a single leader for a partition, any consumers that are on the side of the leader will be able to continue working. Consumers that are on the other side will fail to heartbeat and will stop being able to work -- even if they could otherwise reach the leader for the topics they were consuming.
The consumers on the side of the Marshal coordination partitions will be able to tell that the other consumers dropped off and will be able to start working. (Of course, this may cause them to overload themselves with too many claims, leading to consumer slowness.)
If the partition is between the consumer and Kafka, the consumers will be unable to consume and will also fail their heartbeat. This is effectively treated as Consumer Death: Unexpected. When the partition heals, the consumers that lost their lock will know (assuming machine time is synchronized) and will abandon their claims.
This system assumes that timestamps are valid. If your machines are not using NTP to synchronize their clocks, you will not be able to get deterministic behavior. Sorry.
Marshal also relies on all actors being good actors. Malicious users can cause the system to act unpredictably or at their choosing.
Here are some questions we've seen. For more, see us on IRC.
This is a design property of Marshal's implementation. We start with the premise that we can capably health check ourself and determine whether or not we are keeping up with our current claims. If that's true, then it doesn't matter how many partitions we have -- we'll be healthy.
This means that we can end up in a state where one consumer has several partitions and another consumer has fewer (or none), but Marshal guarantees that all of them will be healthy.
This usually happens when you are reusing Client IDs and your consumer has previously become unhealthy and released partitions. A sick consumer will not reclaim partitions it has previously released.
Make sure you have multiple consumers with different Client IDs, or make sure that in the single consumer use case you are using randomly generated Client IDs every time your program starts.
There may be bugs. This is a new project. There are tests, however, and we very much welcome the submission of bug reports, pull requests, etc.
Github: https://github.com/zorkian/marshal
IRC: #kafka-marshal on Freenode
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