Buffering & Storage
The end-goal of Fluent Bit is to collect, parse, filter and ship logs to a central place. In this workflow there are many phases and one of the critical pieces is the ability to do buffering : a mechanism to place processed data into a temporary location until is ready to be shipped.
By default when Fluent Bit processes data, it uses Memory as a primary and temporary place to store the records, but there are certain scenarios where it would be ideal to have a persistent buffering mechanism based in the filesystem to provide aggregation and data safety capabilities.
Choosing the right configuration is critical and the behavior of the service can be conditioned based in the backpressure settings. Before we jump into the configuration let's make sure we understand the relationship between Chunks, Memory, Filesystem and Backpressure.
Understanding the chunks, buffering and backpressure concepts is critical for a proper configuration. Let's do a recap of the meaning of these concepts.
When an input plugin (source) emits records, the engine groups the records together in a Chunk. A Chunk size usually is around 2MB. By configuration, the engine decides where to place this Chunk, the default is that all chunks are created only in memory.
As mentioned above, the Chunks generated by the engine are placed in memory but this is configurable.
If memory is the only mechanism set for the input plugin, it will just store data as much as it can there (memory). This is the fastest mechanism with the least system overhead, but if the service is not able to deliver the records fast enough because of a slow network or an unresponsive remote service, Fluent Bit memory usage will increase since it will accumulate more data than it can deliver.
In a high load environment with backpressure the risks of having high memory usage is the chance of getting killed by the Kernel (OOM Killer). A workaround for this backpressure scenario is to limit the amount of memory in records that an input plugin can register, this configuration property is called
mem_buf_limit: if a plugin has enqueued more than the
mem_buf_limit, it won't be able to ingest more until that data can be delivered or flushed properly. In this scenario the input plugin in question is paused.
The workaround of
mem_buf_limitis good for certain scenarios and environments, it helps to control the memory usage of the service, but at the costs that if a file gets rotated while paused, you might lose that data since it won't be able to register new records. This can happen with any input source plugin. The goal of
mem_buf_limitis memory control and survival of the service.
For full data safety guarantee, use filesystem buffering.
Here is an example input definition:
If this input uses more than 50MB memory to buffer logs, you will get a warning like this in the Fluent Bit logs:
[input] tcp.1 paused (mem buf overlimit)
Filesystem buffering enabled helps with backpressure and overall memory control.
Behind the scenes, Memory and Filesystem buffering mechanisms are not mutually exclusive, indeed when enabling filesystem buffering for your input plugin (source) you are getting the best of the two worlds: performance and data safety.
When the Filesystem buffering is enabled, the behavior of the engine is different, upon Chunk creation, it stores the content in memory but also it maps a copy on disk (through mmap(2)), this Chunk is active in memory and backed up in disk is called to be
upwhich means "the chunk content is up in memory".
How this Filesystem buffering mechanism deals with high memory usage and backpressure ?: Fluent Bit controls the number of Chunks that are
By default, the engine allows to have 128 Chunks
upin memory in total (considering all Chunks), this value is controlled by service property
storage.max_chunks_up. The active Chunks that are
upare ready for delivery and the ones that still are receiving records. Any other remaining Chunk is in a
downstate, which means that's only in the filesystem and won't be
upin memory unless is ready to be delivered.
If the input plugin has enabled
filesystem, when reaching the
mem_buf_limitthreshold, instead of the plugin being paused, all new data will go to Chunks that are
downin the filesystem. This allows to control the memory usage by the service but also providing a a guarantee that the service won't lose any data.
storage.pause_on_chunks_overlimitis enabled the input plugin will be paused upon exceeding
storage.max_chunks_up. Any down chunks that make it through will still be saved to the filesystem.
Limiting Filesystem space for Chunks
Fluent Bit implements the concept of logical queues: based on its Tag a Chunk, can be routed to multiple destinations, so internally we keep a reference from where a Chunk was created and where it needs to go.
It's common to find cases where if we have multiple destinations for a Chunk, one of the destinations might be slower than the other, or maybe one is generating backpressure and not all of them. In this scenario, how do we limit the amount of filesystem Chunks that we are logically queueing?
Starting from Fluent Bit v1.6, we introduced the new configuration property for output plugins called
storage.total_limit_sizewhich limits the number of Chunks that exist in the file system for a certain logical output destination. If one of destinations reaches the
storage.total_limit_size, the oldest Chunk from its queue for that logical output destination will be discarded.
The storage layer configuration takes place in three areas:
- Service Section
- Input Section
- Output Section
The known Service section configures a global environment for the storage layer, the Input sections define which buffering mechanism to use and the output the limits for the logical queues.
a Service section will look like this:
that configuration sets an optional buffering mechanism where the route to the data is /var/log/flb-storage/, it will use normal synchronization mode, without running a checksum and up to a maximum of 5MB of memory when processing backlog data.
Optionally, any Input plugin can configure their storage preference, the following table describes the options available:
The following example configures a service that offers filesystem buffering capabilities and two Input plugins being the first based in filesystem and the second with memory only.
If certain chunks are filesystem storage.type based, it's possible to control the size of the logical queue for an output plugin. The following table describes the options available:
The following example create records with CPU usage samples in the filesystem and then they are delivered to Google Stackdriver service limiting the logical queue (buffering) to 5M:
If for some reason Fluent Bit gets offline because of a network issue, it will continue buffering CPU samples but just keep a maximum of 5M of the newest data.