InfluxDB Relay

This project adds a basic high availability layer to InfluxDB. With the right architecture and disaster recovery processes, this achieves a highly available setup.

NOTE: influxdb-relay must be built with Go 1.5+

Usage

To build from source and run:

$ # Install influxdb-relay to your $GOPATH/bin
$ go install github.com/influxdata/influxdb-relay
$ # Edit your configuration file
$ cp $GOPATH/github.com/influxdata/influxdb-relay/sample.toml ./relay.toml
$ vim relay.toml
$ # Start relay!
$ $GOPATH/influxdb-relay -config relay.toml

Configuration

[[http]]
# Name of the HTTP server, used for display purposes only.
name = "example-http"

# TCP address to bind to, for HTTP server.
bind-addr = "127.0.0.1:9096"

# Array of InfluxDB instances to use as backends for Relay.
output = [
    # name: name of the backend, used for display purposes only.
    # location: full URL of the /write endpoint of the backend
    # timeout: Go-parseable time duration. Fail writes if incomplete in this time.
    { name="local1", location="http://127.0.0.1:8086/write", timeout="10s" },
    { name="local2", location="http://127.0.0.1:7086/write", timeout="10s" },
]

[[udp]]
# Name of the UDP server, used for display purposes only.
name = "example-udp"

# UDP address to bind to.
bind-addr = "127.0.0.1:9096"

# Socket buffer size for incoming connections.
read-buffer = 0 # default

# Precision to use for timestamps
precision = "n" # Can be n, u, ms, s, m, h

# Array of InfluxDB instances to use as backends for Relay.
output = [
    # name: name of the backend, used for display purposes only.
    # location: host and port of backend.
    # mtu: maximum output payload size
    { name="local1", location="127.0.0.1:8089", mtu=512 },
    { name="local2", location="127.0.0.1:7089", mtu=1024 },
]

Description

The architecture is fairly simple and consists of a load balancer, two or more InfluxDB Relay processes and two or more InfluxDB processes. The load balancer should point UDP traffic and HTTP POST requests with the path /write to the two relays while pointing GET requests with the path /query to the two InfluxDB servers.

The setup should look like this:

        ┌─────────────────┐                 
        │writes & queries │                 
        └─────────────────┘                 
                 │                          
                 ▼                          
         ┌───────────────┐                  
         │               │                  
┌────────│ Load Balancer │─────────┐        
│        │               │         │        
│        └──────┬─┬──────┘         │        
│               │ │                │        
│               │ │                │        
│        ┌──────┘ └────────┐       │        
│        │ ┌─────────────┐ │       │┌──────┐
│        │ │/write or UDP│ │       ││/query│
│        ▼ └─────────────┘ ▼       │└──────┘
│  ┌──────────┐      ┌──────────┐  │        
│  │ InfluxDB │      │ InfluxDB │  │        
│  │ Relay    │      │ Relay    │  │        
│  └──┬────┬──┘      └────┬──┬──┘  │        
│     │    |              |  │     │        
│     |  ┌─┼──────────────┘  |     │        
│     │  │ └──────────────┐  │     │        
│     ▼  ▼                ▼  ▼     │        
│  ┌──────────┐      ┌──────────┐  │        
│  │          │      │          │  │        
└─▶│ InfluxDB │      │ InfluxDB │◀─┘        
   │          │      │          │           
   └──────────┘      └──────────┘           

The relay will listen for HTTP or UDP writes and write the data to both servers via their HTTP write endpoint. If the write is sent via HTTP, the relay will return a success response as soon as any of the InfluxDB servers returns a success. If one of the InfluxDB servers returns a 4xx response, that will be returned to the client immediately. If all servers return a 5xx response, the first one received by the relay will be returned to the client, unless buffering is enabled.

With this setup a failure of one Relay or one InfluxDB can be sustained while still taking writes and serving queries. However, the recovery process might require operator intervention.

Buffering

The relay can be configured to buffer failed requests for HTTP backends. The intent of this logic is reduce the number of failures during short outages or periodic network issues.

This retry logic is NOT sufficient for for long periods of downtime as all data is buffered in RAM

Buffering has the following configuration options (configured per HTTP backend):

• buffer-size-mb -- An upper limit on how much point data to keep in memory (in MB)
• max-batch-kb -- A maximum size on the aggregated batches that will be submitted (in KB)
• max-delay-interval -- the max delay between retry attempts per backend. The initial retry delay is 500ms and is doubled after every failure.

If the buffer is full then requests are dropped and an error is logged. If a requests makes it into the buffer it is retried until success.

Retries are serialized to a single backend. In addition, writes will be aggregated and batched as long as the body of the request will be less than max-batch-kb If buffered requests succeed then there is no delay between subsequent attempts.

If the relay stays alive the entire duration of a downed backend server without filling that server's allocated buffer, and the relay can stay online until the entire buffer is flushed, it would mean that no operator intervention would be required to "recover" the data. The data will simply be batched together and written out to the recovered server in the order it was received.

NOTE: The limits for buffering are not hard limits on the memory usage of the application, and there will be additional overhead that would be much more challenging to account for. The limits listed are just for the amount of point line protocol (including any added timestamps, if applicable). Factors such as small incoming batch sizes and a smaller max batch size will increase the overhead in the buffer. There is also the general application memory overhead to account for. This means that a machine with 2GB of memory should not have buffers that sum up to almost 2GB.

Recovery

InfluxDB organizes its data on disk into logical blocks of time called shards. We can use this to create a hot recovery process with zero downtime.

The length of time that shards represent in InfluxDB range from 1 hour to 7 days. For retention policies with an infinite duration (that is they keep data forever), their shard durations are 7 days. For the sake of our example, let's assume shard sizes of 1 day.

Let's say one of the InfluxDB servers goes down for an hour on 2016-03-10. Once the next day rolls over and we're now writing data to 2016-03-11, we can then restore things using these steps:

1. Create backup of 2016-03-10 shard from server that was up the entire day
2. Tell the load balancer to stop sending query traffic to the server that was down
3. Restore the backup of the shard from the good server to the old server
4. Tell the load balancer to resume sending queries to the previously downed server

During this entire process the Relays should be sending writes to both servers for the current shard (2016-03-11).

Sharding

It's possible to add another layer on top of this kind of setup to shard data. Depending on your needs you could shard on the measurement name or a specific tag like customer_id. The sharding layer would have to service both queries and writes.

Caveats

While influxdb-relay does provide some level of high availability, there are a few scenarios that need to be accounted for:

• influxdb-relay will not relay the /query endpoint, and this includes schema modification (create database, DROPs, etc). This means that databases must be created before points are written to the backends.
• Continuous queries will still only write their results locally. If a server goes down, the continuous query will have to be backfilled after the data has been recovered for that instance.
• Overwriting points is potentially unpredictable. For example, given servers A and B, if B is down, and point X is written (we'll call the value X1) just before B comes back online, that write is queued behind every other write that occurred while B was offline. Once B is back online, the first buffered write succeeds, and all new writes are now allowed to pass-through. At this point (before X1 is written to B), X is written again (with value X2 this time) to both A and B. When the relay reaches the end of B's buffered writes, it will write X (with value X1) to B... At this point A now has X2, but B has X1.
  • It is probably best to avoid re-writing points (if possible). Otherwise, please be aware that overwriting the same field for a given point can lead to data differences.
  • This could potentially be mitigated by waiting for the buffer to flush before opening writes back up to being passed-through.