深入JetStream模型
Stream Limits, Retention, and Policy
Streams store data on disk, but we cannot store all data forever so we need ways to control their size automatically.
There are 3 features that come into play when Streams decide how long they store data.
The Retention Policy
describes based on what criteria a set will evict messages from its storage:
In all Retention Policies the basic limits apply as upper bounds, these are MaxMsgs
for how many messages are kept in total, MaxBytes
for how big the set can be in total and MaxAge
for what is the oldest message that will be kept. These are the only limits in play with LimitsPolicy
retention.
One can then define additional ways a message may be removed from the Stream earlier than these limits. In WorkQueuePolicy
the messages will be removed as soon as the Consumer received an Acknowledgement. In InterestPolicy
messages will be removed as soon as all Consumers of the stream for that subject have received an Acknowledgement for the message.
In both WorkQueuePolicy
and InterestPolicy
the age, size and count limits will still apply as upper bounds.
A final control is the Maximum Size any single message may have. NATS have it's own limit for maximum size (1 MiB by default), but you can say a Stream will only accept messages up to 1024 bytes using MaxMsgSize
.
The Discard Policy
sets how messages are discarded when limits set by LimitsPolicy
are reached. The DiscardOld
option removes old messages making space for new, while DiscardNew
refuses any new messages.
The WorkQueuePolicy
mode is a specialized mode where a message, once consumed and acknowledged, is removed from the Stream.
Message Deduplication
JetStream support idempotent message writes by ignoring duplicate messages as indicated by the Nats-Msg-Id
header.
Here we set a Nats-Msg-Id:1
header which tells JetStream to ensure we do not have duplicates of this message - we only consult the message ID not the body.
and in the output you can see that the duplicate publications were detected and only one message (the first one) is actually stored in the stream
The default window to track duplicates in is 2 minutes, this can be set on the command line using --dupe-window
when creating a stream, though we would caution against large windows.
Acknowledgement Models
Streams support acknowledging receiving a message, if you send a Request()
to a subject covered by the configuration of the Stream the service will reply to you once it stored the message. If you just publish, it will not. A Stream can be set to disable Acknowledgements by setting NoAck
to true
in it's configuration.
Consumers have 3 acknowledgement modes:
To understand how Consumers track messages we will start with a clean ORDERS
Stream and DISPATCH
Consumer.
The Set is entirely empty
The Consumer has no messages outstanding and has never had any (Consumer sequence is 1).
We publish one message to the Stream and see that the Stream received it:
As the Consumer is pull-based, we can fetch the message, ack it, and check the Consumer state:
The message got delivered and acknowledged - Acknowledgement floor
is 1
and 1
, the sequence of the Consumer is 2
which means its had only the one message through and got acked. Since it was acked, nothing is pending or redelivering.
We'll publish another message, fetch it but not Ack it this time and see the status:
Get the next message from the consumer (but do not acknowledge it)
Show the consumer info
Now we can see the Consumer has processed 2 messages (obs sequence is 3, next message will be 3) but the Ack floor is still 1 - thus 1 message is pending acknowledgement. Indeed this is confirmed in the Pending messages
.
If I fetch it again and again do not ack it:
Show the consumer info again
The Consumer sequence increases - each delivery attempt increases the sequence - and our redelivered count also goes up.
Finally, if I then fetch it again and ack it this time:
Show the consumer info
Having now Acked the message there are no more pending.
Additionally, there are a few types of acknowledgements:
So far all of the examples were the AckAck
type of acknowledgement, by replying to the Ack with the body as indicated in Bytes
you can pick what mode of acknowledgement you want.
All of these acknowledgement modes, except AckNext
, support double acknowledgement - if you set a reply subject when acknowledging the server will in turn acknowledge having received your ACK.
The +NXT
acknowledgement can have a few formats: +NXT 10
requests 10 messages and +NXT {"no_wait": true}
which is the same data that can be sent in a Pull Request.
Exactly Once Semantics
JetStream supports Exactly Once publication and consumption by combining Message Deduplication and double acks.
On the publishing side you can avoid duplicate message ingestion using the Message Deduplication feature.
Consumers can be 100% sure a message was correctly processed by requesting the server Acknowledge having received your acknowledgement (sometimes referred to as double-acking) by calling the message's AckSync()
(rather than Ack()
) function which sets a reply subject on the Ack and waits for a response from the server on the reception and processing of the acknowledgement. If the response received from the server indicates success you can be sure that the message will never be re-delivered by the consumer (due to a loss of your acknowledgement).
Consumer Starting Position
When setting up a Consumer you can decide where to start, the system supports the following for the DeliverPolicy
:
Regardless of what mode you set, this is only the starting point. Once started it will always give you what you have not seen or acknowledged. So this is merely how it picks the very first message.
Let's look at each of these, first we make a new Stream ORDERS
and add 100 messages to it.
Now create a DeliverAll
pull-based Consumer:
Now create a DeliverLast
pull-based Consumer:
Now create a MsgSetSeq
pull-based Consumer:
And finally a time-based Consumer. Let's add some messages a minute apart:
Then create a Consumer that starts 2 minutes ago:
Ephemeral Consumers
So far, all the Consumers you have seen were Durable, meaning they exist even after you disconnect from JetStream. In our Orders scenario, though the MONITOR
a Consumer could very well be a short-lived thing there just while an operator is debugging the system, there is no need to remember the last seen position if all you are doing is wanting to observe the real-time state.
In this case, we can make an Ephemeral Consumer by first subscribing to the delivery subject, then creating a durable and giving it no durable name. An Ephemeral Consumer exists as long as any subscription is active on its delivery subject. It is automatically be removed, after a short grace period to handle restarts, when there are no subscribers.
Terminal 1:
Terminal 2:
The --ephemeral
switch tells the system to make an Ephemeral Consumer.
Consumer Message Rates
Typically what you want is if a new Consumer is made the selected messages are delivered to you as quickly as possible. You might want to replay messages at the rate they arrived though, meaning if messages first arrived 1 minute apart and you make a new Consumer it will get the messages a minute apart.
This is useful in load testing scenarios etc. This is called the ReplayPolicy
and have values of ReplayInstant
and ReplayOriginal
.
You can only set ReplayPolicy
on push-based Consumers.
Now let's publish messages into the Set 10 seconds apart:
And when we consume them they will come to us 10 seconds apart:
Ack Sampling
In the earlier sections we saw that samples are being sent to a monitoring system. Let's look at that in depth; how the monitoring system works and what it contains.
As messages pass through a Consumer you'd be interested in knowing how many are being redelivered and how many times but also how long it takes for messages to be acknowledged.
Consumers can sample Ack'ed messages for you and publish samples so your monitoring system can observe the health of a Consumer. We will add support for this to NATS Surveyor.
Configuration
You can configure a Consumer for sampling bypassing the --sample 80
option to nats consumer add
, this tells the system to sample 80% of Acknowledgements.
When viewing info of a Consumer you can tell if it's sampled or not:
Output contains
Storage Overhead
JetStream file storage is very efficient, storing as little extra information about the message as possible.
We do store some message data with each message, namely:
Message headers
The subject it was received on
The time it was received
The message payload
A hash of the message
The message sequence
A few other bits like the length of the subject and the length of headers
Without any headers the size is:
A 5 byte hello
message without headers will take 39 bytes.
With headers:
So if you are publishing many small messages the overhead will be, relatively speaking, quite large, but for larger messages the overhead is very small. If you publish many small messages it's worth trying to optimize the subject length.
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