Here are Faraday's contributions to open source that we use every day in production. No experiments here; this is the stuff that we looked for on the shelf, found the options wanting, and built ourselves.
A new standard for secrets: Secretfile
secret_garden (Ruby), vault-env (JS), and credentials-to-env (Rust) all implement a standard we call Secretfile(s):
# /app/Secretfile
DATABASE_URL secrets/database/$VAULT_ENV:url
REDIS_URL secrets/redis/$VAULT_ENV:url
Then you use it like this SecretGarden.fetch('DATABASE_URL').
Clients implementing this standard are meant to first check the environment for DATABASE_URL, then failing that look up the secret in Hashicorp Vault (interpolating $VAULT_ENV into production, staging, etc. first). It's very useful for development where your DATABASE_URL is just postgres://seamus@127.0.0.1:5432/myapp - you can save this in a local .env file and only mess with Vault in production/staging.
Lightning fast CSV processing: catcsv and scrubcsv
catcsv is a very fast CSV concatenation tool that gracefully handles headers and compression. It also supports Google's Snappy compression. We store everything on S3 and GCS szip'ed using burntsushi's szip.
$ cat a.csv
city,state
burlington,vt
$ cat b.csv
city,state
madison,wi
$ szip a.csv
$ ls
a.csv.sz
b.csv
$ catcsv a.csv.sz b.csv
city,state
burlington,vt
madison,wi
Of course, before you cat files, sometimes you need to clean them up with scrubcsv:
$ scrubcsv giant.csv > scrubbed.csv
3000001 rows (1 bad) in 51.58 seconds, 72.23 MiB/sec
Lightning-fast fixed-width to CSV: fixed2csv
fixed2csv converts fixed-width files to CSV very fast. You start with this:
first last middle
John Smith Q
Sally Jones
You should be able to run:
$ fixed2csv -v 10 10 6 < input.txt
first,last,middle
John,Smith,Q
Sally,Jones,
World's fastest geocoder: node_smartystreets
node_smartystreets is the world's fastest geocoder client. We shell out to its binary rather than using it as a library. It will do 10k records/second against the smartystreets geocoding API. If you don't have an Unlimited plan, use it with extreme caution.
Better caching: lock_and_cache
lock_and_cache (Ruby) and lock_and_cache_js (JS) go beyond normal caching libraries: they lock the calculation while it's being performed. Most caching libraries don't do locking, meaning that >1 process can be calculating a cached value at the same time. Since you presumably cache things because they cost CPU, database reads, or money, doesn't it make sense to lock while caching?
def expensive_thing
@expensive_thing ||= LockAndCache.lock_and_cache("expensive_thing/#{id}", expires: 30) do
# do expensive calculation
end
end
It uses Redis for distributed caching and locking, so this is not only cross-process but also cross-machine.
Better state machine: status_workflow
status_workflow handles state transitions with distributed locking using Redis. Most state machine libraries either don't do locking or use Postgres advisory locks.
class Document < ActiveRecord::Base
include StatusWorkflow
status_workflow(
archive_requested: [:archiving],
archiving: [:archived],
)
end
Then you can do
document.enter_archive_requested!
It's safe to use in a horizontally sharded environment because it uses distributed locking - the second process that tries to do this will get a InvalidTransition error even if it's the same microsecond.
Rust build tools: rust-musl-builder and heroku-buildpack-rust
rust-musl-builder is how we build Rust apps on top of Alpine. It also drives heroku-buildpack-rust, the preeminent way of running Rust on Heroku.
Minimal postgres for node: simple-postgres
simple-postgres (JS) is just the essentials to talk to Postgres from Node. We particularly love its use of template literals for apparently magical escaping:
let account = await db.row`
SELECT *
FROM accounts
WHERE id = ${id}
`
Yes, that's safe!
Minimal HTTP server: srvr
srvr (JS) is a small HTTP server that speaks for itself:
- everything express does
- better
- less code
- no dependencies
- websockets
Proper Docker API support for Rust: boondock
boondock is a rewrite of rust-docker to be more correct.
Coordinate docker-compose: cage
cage boots multiple docker-compose.ymls, each as a pod. It's sortof like a local k8s. You configure it with a bunch of docker-compose files:
pods/
├── admin.yml (a pod containing adminweb and horse)
├── common.env (common env vars)
├── donkey.yml (a pod containing donkey)
├── placeholders.yml (development-only pod with redis, db, etc.)
[...]
Local development looks like this:
$ cage pull
==== Fetching secrets from vault into config/secrets.yml
==== Logging into ECR
Fetching temporary AWS 'administrator' credentials from vault
Pulling citus ... done
Pulling citusworker1 ... done
Pulling citusworker2 ... done
Pulling queue ... done
Pulling redis ... done
Pulling s3 ... done
Pulling smtp ... done
Pulling vault ... done
Pulling horse ... done
Pulling adminweb ... done
[...]
$ cage up
Starting fdy_citusworker2_1 ... done
Starting fdy_smtp_1 ... done
Starting fdy_citus_1 ... done
Starting fdy_vault_1 ... done
Starting fdy_citusworker1_1 ... done
Starting fdy_queue_1 ... done
Starting fdy_s3_1 ... done
Starting fdy_redis_1 ... done
Starting fdy_horse_1 ... done
Starting fdy_adminweb_1 ... done
[...]
$ cage stop
Stopping fdy_citusworker2_1 ... done
Stopping fdy_vault_1 ... done
Stopping fdy_citus_1 ... done
Stopping fdy_s3_1 ... done
[...]
Fixed up rust crates: rust-amqp
rust-amqp@tokio (Rust) is our rewrite of the internals of the rust-amqp crate in proper tokio. It is much more reliable and needs to be merged upstream.
(beta release) 3rd gen batch processing on k8s: falconeri
falconeri is a distributed batch job runner for kubernetes (k8s). It is compatible with Pachyderm pipeline definitions, but is simpler and handles autoscaling, etc. properly.
(alpha release) Seamless transfer between Postgres/Citus and BigQuery: dbcrossbar
dbcrossbar handles all the details of transferring tables and data to and from Postgres and Google BigQuery. Additionally, it knows about citus, the leading Postgres horizontal sharding solution - so it can do highly efficient transfers between Citus clusters and BigQuery.
Conclusion
That's it. I only mentioned tools that we use every day.
