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 process data, it uses Memory as a primary and temporary place to store the records, but there are certain scenarios where 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 to jump into the configuration properties let's understand the relationship between Chunks, Memory, Filesystem and Backpressure.
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.
Chunks
When an input plugin (source) emit records, the engine group the records together in a Chunk. A Chunk size usually is around 2MB. By configuration, the engine decide where to place this Chunk, the default is that all chunks are created only in memory.
Buffering and 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 less 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.
On a high load environment with backpressure the risks of having high memory usage is the chance to get 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 have enqueued more than mem_buf_limit, it won't be able to ingest more until it data can be delivered or flushed properly. On this scenario the input plugin in question is paused.
The workaround of mem_buf_limit is 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_limit is memory control and survival of the service.
For full data safety guarantee, use filesystem buffering.
Filesystem buffering to the rescue
Filesystem buffering enabled helps with backpressure and overall memory control.
Behind the scenes, Memory and Filesystem buffering mechanisms are not mutual 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 up which 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 up in memory.
By default, the engine allows to have 128 Chunks up in memory in total (considering all Chunks), this value is controlled by service property storage.max_chunks_up. The active Chunks that are up are ready for delivery and the ones that still are receiving records. Any other remaining Chunk is in a down state, which means that's only in the filesystem and won't be up in memory unless is ready to be delivered.
If the input plugin has enabled mem_buf_limit and storage.type as filesystem, when reaching the mem_buf_limit threshold, instead of the plugin being paused, all new data will go to Chunks that are down in 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.
Limiting Filesystem space for Chunks
Fluent Bit implements the concept of logical queues: a Chunk based on its Tag, 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 that if we have multiple destinations for a Chunk, one of the destination might be slower than the other, and maybe one of the destinations is generating backpressure and not all of them. On 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_size which limits the number of Chunks that exists in the file system for a certain logical output destination. If one destinations reaches the storage.total_limit_size limit, the oldest Chunk from it queue for that logical output destination will be discarded.
Configuration
The storage layer configuration takes place in three areas:
Service Section
Input Section
Output Section
The known Service section configure a global environment for the storage layer, the Input sections defines which buffering mechanism to use and the output the limits for the logical queues.
Service Section Configuration
The Service section refers to the section defined in the main configuration file:
Key | Description | Default |
storage.path | Set an optional location in the file system to store streams and chunks of data. If this parameter is not set, Input plugins can only use in-memory buffering. | |
storage.sync | Configure the synchronization mode used to store the data into the file system. It can take the values normal or full. | normal |
storage.checksum | Enable the data integrity check when writing and reading data from the filesystem. The storage layer uses the CRC32 algorithm. | Off |
storage.max_chunks_up | If the input plugin has enabled | 128 |
storage.backlog.mem_limit | If storage.path is set, Fluent Bit will look for data chunks that were not delivered and are still in the storage layer, these are called backlog data. This option configure a hint of maximum value of memory to use when processing these records. | 5M |
storage.metrics | If | off |
a Service section will look like this:
that configuration configure an optional buffering mechanism where it root for data is /var/log/flb-storage/, it will use normal synchronization mode, without checksum and up to a maximum of 5MB of memory when processing backlog data.
Input Section Configuration
Optionally, any Input plugin can configure their storage preference, the following table describe the options available:
Key | Description | Default |
storage.type | Specify the buffering mechanism to use. It can be memory or filesystem. | memory |
The following example configure a service that offers filesystem buffering capabilities and two Input plugins being the first based in filesystem and the second with memory only.
Output Section Configuration
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 describe the options available:
Key | Description | Default |
storage.total_limit_size | Limit the maximum number of Chunks in the filesystem for the current output logical destination. |
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 continuing buffering CPU samples but just keeping a maximum of 5M of the newest data.
Last updated