I am using the following pipeline to forward data
Auditbeat ---> logstash ---> ES
Suppose if the logstash machine goes down, I want to know how the Auditbeat handles the situation.
I would like to know the specifics like
is there a retry mechanism?
how long will it retry?
what happens to the audit logs, will it be lost?
the reason that I ask question 3 is that, we enable auditbeat by disabling auditd service (which was generating the auditlogs under /var/log/audit/audit.log). SO
if logstash goes down there is no data forwarding happening and hence there is a chance of data loss. Please clarify.
if auditbeat is storing the data while logstash is down, where is it doing so? and what is the memory(disk space) allocated to this saving process?
Thanks in advance
Auditbeat has an internal queue which stores the events before sending it to the configured output, by default this queue is a memory queue that will store up to 4096 events.
If the queue is full, no more events will be stored until the output comes back and start to receive data from auditbeat, there is a risk of data loss here.
You can change the number of the events that the memory queue stores.
There is also the option to use a file queue, which will save the events to disk before sending to the configured output, but this feature is still in beta.
You can read about the internal queue in the documentation.
Related
I've been using Flink and kinesis analytics recently.
I have a stream of data and also I need a cache to be shared with the stream.
To share the cache data with the kinesis stream, it's connected to a broadcast stream.
The cache source extends SourceFunction and implements ProcessingTimeCallback. Gets the data from DynamoDB every 300 seconds and broadcast it to the next stream using KeyedBroadcastProcessFuction.
But after adding the broadcast stream (in the previous version I hadn't
a cache and I was using KeyedProcessFuction for kinesis stream), when I execute it in kinesis analytics, it keeps restarting about every 1000 seconds without any exception!
I have no configuration with this value and the scenario works fine in between!
Could anybody help me what could be the issue?
My first thought is to wonder if this might be related to checkpointing. Do you have access to the server logs? Flink's logging should make it somewhat clear what's causing the restart.
The reason why I suspect checkpointing is that it occurs at predictable times (and with a long timeout), and using broadcast state can put a lot of pressure on checkpointing. Each parallel instance will checkpoint a full copy of the broadcast state.
Broadcast state has to be kept on-heap, so another possibility is that you are running out of memory.
We have ELK(+XPACK) for our network devices syslog server (source/destination IP and port). I'm trying to implement a real-time alerting system when source_ip field equals a specific IP address. How can i accomplish this with ELK?
I tried to do it with watcher, but it isn't real-time and low intervals may cause performance problems(?).
Note: log rate ~ 500 log per second.
If watcher are not fast enough, then you need sth. what will fire at the moment of data incoming. Ingest pipelines can't execute external actions, but if you have Logstah in place, then clone (https://www.elastic.co/guide/en/logstash/current/plugins-filters-clone.html) the relevant event and issue an alert via email (https://www.elastic.co/guide/en/logstash/current/plugins-outputs-email.html) or whatever suits you.
Doing so, there will be the original event in elastic and the cloned one can be processed i a separate alert pipeline.
Recently i have been reading into Elastic stack and finding out about this thing called Beats, which basically used for lightweight shippers.
So the question is, if my service can directly hit to Elasticsearch, do i actually need beats for it? Since from what i have known it's just kinda a proxy (?)
Hopefully my question is clear enough
Not sure which beat you are specifically referring but let's take an example of Filebeat.
Suppose application logs need to be indexed into Elasticsearch. Options
Post the logs directly to Elasticsearch
Save the logs to a file, then use Filebeat to index logs
Publish logs to a AMQP service like RabbitMQ or Kafka, then use Logstash input plugins to read from RabbitMQ or Kafka and index into Elasticsearch
Option 2 Benefits
Filebeat ensures that each log message got delivered at-least-once. Filebeat is able to achieve this behavior because it stores the delivery state of each event in the registry file. In situations where the defined output is blocked and has not confirmed all events, Filebeat will keep trying to send events until the output acknowledges that it has received the events.
Before shipping data to Elasticsearh, we can do some additional processing or filtering. We want to drop some logs based on some text in the log message or add additional field (eg: Add Application Name to all logs, so that we can index multiple application logs into single index, then on consumption side we can filter the logs based on application name.)
Essentially beats provide the reliable way of indexing data without causing much overhead to the system as beats are lightweight shippers.
Option 3 - This also provides the same benefits as option2. This might be more useful in case if we want to ship the logs directly to an external system instead of storing it in a file in the local system. For any applications deployed in Docker/Kubernetes, where we do not have much access or enough space to store files in the local system.
Beats are good as lightweight agents for collecting streaming data like log files, OS metrics, etc, where you need some sort of agent to collect and send. If you have a service that wants to put things into Elastic, then yes by all means it can just use rest/java etc API directly.
Filebeat offers a way to centralize live logs from Multiple Servers
Let's say you are running multiple instances of an application in different servers and they are writing logs.
You can ship all these logs to a single ElasticSearch index and analyze or visualize them from there.
A single static file doesn't need Filebeat for moving to ElasticSearch.
I have read about the at-least-once-delivery commitment of filebeat and what I understood is that until the ack of sent logline is not received by filebeat, that line will be sent again (in case of filebeat re-start).
Now supppose, In my solution, I am using Filebeat, Logstash, and one other component that logstash is using for filtering. And after filtering the logstash sends the line to elasticsearch.
Now here are below checkpoints where we can loss data :
Filebeat got shutdown without receiving ack from logstash - In this case we know that line will be sent again by filebeat.
Suppose Filebeat sent a line, and logstash applies filtering on it with the external component and then when It tries to send to elasticsearch and the same time logstash/elasticsearch got crashed, So will we loss this data.
My question is:
Basically logstash processes data in below sequence:
INPUT --> FILTER --> OUTPUT
So I want to know at which step the logstash will send ACK to filebeat. I want to basically understand how the ACKS are being sent and when. I tried to search it on google and ELK official websites but didn't get the information in details.
Can somebody help me in understanding these details ?
thanks in advance.
The input will ACK when it pushes the events to the internal queue for the pipeline workers. That's when the plugin-input thread considers the event to be completed.
What happens with the pipeline workers kind of depends. If you have persistent queues configured and enabled, those jobs will be picked up again once logstash restarts and no data should be lost (if it is, that's a bug). If you don't have persistent queues, then that data will be lost.
I am going to be using logstash to send a high amount of events to a broker. I have monitoring of the broker to check the health status, but I can't find much information on how to see if the logstash process is healthy, if there are indicators of a failing process.
I was interested for those who use logstash, what are some ways you monitor it?
You can have a cronjob inject a heartbeat message and route such messages to some kind of monitoring system. If you already use Elasticsearch you could use it for this as well and write a script to ensure that you have reasonably recent heartbeat messages from all hosts that should be sending messages, but I'd prefer using e.g. Nagios or lovebeat-go.
This could be used to monitor the health of a single Logstash instance (i.e. you inject the heartbeat message into the same instance that feeds the monitoring software) but you could just as well use it to check the overall health of the whole pipeline.
Update: This got built into Logstash in 2015. See the announcement of the Logstash heartbeat plugin.
If you're trying to monitor logstash as a shipper, it's easy to write a script that would compare the contents of the .sincedb* file to the actual file on disk to make sure they're in sync.
As an indexer, I'd probably skip ahead and query ElasticSearch for the number of documents being inserted.
#magnus' idea for a latency check is also good. I've used the log's timestamp and compared it to ElasticSearch's timestamp to compute the latency.