Coolest PaaS/IaaS I've ever use: Jelastic

So, I'm looking an simplest deployment strategy for my next side project, I don't want to use Kubernetes since I'm all alone XD, learning Nomad, WayPoint, Swarm, and other popular tool to make it easy like Portainer, but why they doesn't make it just as simple as Vercel or Fly.io). Also don't want to use big cloud providers (GCP, AWS, Azure, etc) which the UI quite sucks like everything developed by different team with lack of communication and you have to do a lot of setup hassle just to deploy simple things. Then I found a really cool product called Jelastic, that fit my needs:

1. Can autoscale out (like AWS ELB/ECS, GCR, ACS, etc) and auto-clustering (as easy as CloudSQL or AWS RDS/Aurora, but can be automatic)
   
2. Can autoscale up '__') without downtime, only took 1 second to scale up from 1 core 640MB to 16 core 32GB (seems like they only changing container's resource quota limit) but you can see the changes directly without restart
3. Can deploy VPS on the same cluster/network (for my databases, since I don't use "standard/popular" databases) and it's super cheap (it only took 3.9$ per month to deploy a VPS with 1 static IP, and can autoscale up), you only need to pay what you utilize (CPU and RAM usage), not charged 100% when server up unlike other VPS providers
4. The UI doesn't sucks XD you can WebSSH, normal SSH (as long as have real IP), easy SSL setup, super easy to change config, the lacking part about Jelastic probably configfile/gitops-based setup (for working with multiple members in the future) at least there's API and CLI to create and modify environment, not sure if there's auditing available (haven't checked yet). 
5. Can also deploy automatically from git (checked every N minutes) or CI pipeline or using CLI.
   
6. Easy to move (live migration) to different providers, change ownership of a cluster, or if it's not enabled, at least there's no vendor locking, you can also manually export and import environment (for example copying staging setup to production has similar architecture just different deployment branch and scaling strategy).

Other cool things that I won't use: deploying any-container/docker-based with easy steps, deploying kubernetes, bunch of stack in marketplace provided (may vary on different provider).

For 3.9$ (if you utilize only 1%) per month (16 core, 32GB RAM, 200 GB NVMe VPS, 1 static IP, provider: ToggleBox), you can get the greenest (highest on average) result among all VPS I've ever tried:

You can see the raw benchmark result here and recap here.

What's the catch?

1. It's quite expensive if you utilize 100% (around 339$ if you use ToggleBox for the specs above), for comparison:
   1. cheapest highest spec Contabo's VPS (9 core, 60GB RAM, 1.5TB SSD) unmetered bandwidth only cost $55-ish per month (not apple-to-apple since it's different spec and performance, also this is what you should pay per month regardless your utilization)
   2. similar spec GCE n1-custom-16-32768 (16 core, 32GB, 200GB SSD) non-committed, cost $525 excluding bandwidth
      
   3. similar spec AWS EC2 a1.xlarge (16 core, 32GB RAM, 200GB gp2 SSD) on-demand, only cost  $317 excluding bandwidth
      
   4. similar spec Azure F16s (16 core, 32GB RAM, 256GB SSD) pay-as-you-go, cost $634 excluding bandwidth
   5. cheapest OVH on SG (8 core. 64GB RAM, 400GB SSD) only cost $135 with unmetered 200Mbps bandwidth
   but still, this is way cheaper for minimum usage than if you use GCR you will be billed around ~$10 per month for idle instance, or ~$37 for standby instance (for 1 VCPU, 1 GB RAM, not including bandwidth that quite pricey $0.085)
2. Some provider have different "free" tier, for example ToggleBox give free 2GB bandwidth per hour (GCR only give free 1GB per month XD), some other provider give free 1 static IP, some other provider give free 10GB disk usage per hour, etc.
3. License might be pricey if you install it on your own cluster instead of using the already provided (eg. DewaCloud or CloudKilat for Indonesia region, ToggleBox for US region, etc), but they have profit sharing model if you are a reseller (have your own VPS and rent it).
4. The billing is hourly (so you will always billed at minimum 1 cloudlet -- specs of 1 cloudlet can be vary per provider), compared to for example GCR that use second as minimum billing resolution (VCPU, GB RAM, Requests, and Bandwidth).

That's it for now, I'll create a new post if I found something better.

Kafka vs RedPanda Benchmark (also Tarantool and Clickhouse as queue)

Using default settings from their docker-compose example, today we're gonna benchmark one of popular MQ/PubSub software. I never used MQ extensively before (only NATS, Google PubSub, ActiveMQ, and Amazon SQS), usually just using standard database that stores event is sufficient (the consumer using pull, tailing from last primary key counter, and if need to fan-out just use multiple goroutine and multiple channel), because my projects never been a latency sensitive applications.

Some issues: 

1. the benchmark has locking (atomic counters, sync.Map, etc), so consumer might not utilize whole CPU cores.
2. confluent's kafka docker always error when starting because /var/lib/kafka/data not writable, so I bind on /var/lib/kafka instead. Clickhouse also always failed to start when bind to /var/lib/clickhouse/data, so I don't bind volume for Clickhouse.
3. RedPanda failed to start when fs.aio-max-nr even when it's already ~1 million (originally only 64K), so I set it to 4194304

Benchmarking 1000 goroutines publishing 2000 messages each, with 100 goroutines consuming in parallel.

REDPANDA version: v21.10.1 (rev e7b6714)

=== redpanda single:

FailProduce:  0

FailConsume:  0

DoubleConsume:  0

Produced (ms):  2387

MaxLatency (ms):  2125

AvgLatency (ms):  432

Total (s) 3.457646367s

FailProduce:  0

FailConsume:  0

DoubleConsume:  0

Produced (ms):  2408

MaxLatency (ms):  2663

AvgLatency (ms):  490

Total (s) 3.459949739s

=== redpanda multi:

FailProduce:  0

FailConsume:  0

DoubleConsume:  0

Produced (ms):  4187

MaxLatency (ms):  12146

AvgLatency (ms):  9701

Total (s) 13.610533861s 

# ^ weird, maybe startup not yet complete?

# retried reinit docker-compose, 1st time always slow

# but 2nd time always fast:

FailProduce:  0

FailConsume:  0

DoubleConsume:  0

Produced (ms):  2413

MaxLatency (ms):  2704

AvgLatency (ms):  467

Total (s) 3.545496041s

KAFKA version: 7.0.0-ccs (Commit:c6d7e3013b411760)

equal to kafka 3.0.0

=== kafka single:

FailProduce:  0

FailConsume:  0

DoubleConsume:  0

Produced (ms):  6634

MaxLatency (ms):  12052

AvgLatency (ms):  8579

Total (s) 13.722706977s

FailProduce:  0

FailConsume:  0

DoubleConsume:  0

Produced (ms):  6380

MaxLatency (ms):  11856

AvgLatency (ms):  8636

Total (s) 13.625928209s

=== kafka multi:

FailProduce:  0

FailConsume:  0

DoubleConsume:  0

Produced (ms):  6596

MaxLatency (ms):  11932

AvgLatency (ms):  8523

Total (s) 13.659630863s

FailProduce:  0

FailConsume:  0

DoubleConsume:  0

Produced (ms):  6535

MaxLatency (ms):  11903

AvgLatency (ms):  8588

Total (s) 13.677644818s

These benchmark using default settings that exists in the docker examples I found, except SMP (I set it to the same amount of cores in the server that used to benchmark to make it fair with Kafka that uses JVM that by default can utilize all cores -- apparently this has insignificant impact). Current conclusion is, RedPanda way faster than Kafka, in terms of publishing speed (around ~1μs per message, 477K-837K msg/s) and consuming latency (432ms to 2.7s per message), while Kafka (around ~3μs per message, 301K-313K msg/s) and 8.5s to 12s per message. The RAM statistics tho, RedPanda uses 12GB for each node (10% of server's RAM), while Kafka only uses 355MB, 375MB, 788MB for nodes, and 120MB for zookeeper. The repo to reproduce this benchmark is here on 2021mq directory.

Btw if you're looking for Kafka/RedPanda GUI, try KOwl, this way more beautiful than ActiveMQ default Web UI.

Bonus rounds, using one of the fastest OLTP database: Tarantool and one of the fastest OLAP database: Clickhouse as Queue, by laveraging sequence (auto increment) or internal function to generate a sequence, the difference is there's only one consumer group (have to manually fan out using goroutine), no json encode and decode since it's structured database:

TARANTOOL version: 2.8.2

=== tarantool single (memtx):

FailProduce:  0

FailConsume:  0

DoubleConsume:  0

Produced (ms):  11238

MaxLatency (ms):  1071

AvgLatency (ms):  101

Total (s) 11.244551225s

FailProduce:  0

FailConsume:  0

DoubleConsume:  0

Produced (ms):  9596

MaxLatency (ms):  816

AvgLatency (ms):  61

Total (s) 9.957516119s

=== tarantool single (vinyl):

FailProduce:  0

FailConsume:  0

DoubleConsume:  0

Produced (ms):  11383

MaxLatency (ms):  1076

AvgLatency (ms):  157

Total (s) 11.388865281s

FailProduce:  0

FailConsume:  0

DoubleConsume:  0

Produced (ms):  9104

MaxLatency (ms):  102

AvgLatency (ms):  13

Total (s) 9.196549551s

CLICKHOUSE version: 21.11.4.14

=== clickhouse single:

FailProduce:  0
FailConsume:  0

DoubleConsume:  0

Produced (ms):  2052

MaxLatency (ms):  2078

AvgLatency (ms):  1461

Total (s) 3.570767491s

FailProduce:  0

FailConsume:  0

DoubleConsume:  0

Produced (ms):  2057

MaxLatency (ms):  2008

AvgLatency (ms):  1445

Total (s) 3.536277427s

The result recap table (ms = millisecond, us = microsecond, ns = nanosecond):

only best of 2 runs	RedPanda single	RedPanda multi	Kafka single	Kafka multi	Tarantool memtx	Tarantool vinyl	Clickhouse single
Publish (ms)	2,387	2,413	6,380	6,535	9,596	9,104	2,052
Sub Max Latency (ms)	2,125	2,704	11,856	11,903	816	102	2,008
Sub Avg Latency (ms)	490	467	8,636	8,523	61	13	1,445
Pub Troughput (msg/s)	837,872	828,844	313,480	306,044	208,420	219,684	974,659
est. Pub Latency (ns)	1,194	1,207	3,190	3,268	4,798	4,552	1,026
est. Sub Throughput (msg/s)	4,081,633	4,282,655	231,589	234,659	32,786,885	153,846,154	1,384,083

Conclusion: Tarantool probably the only single node database that can compete with Kafka for queue use case (we can have multi-master replica but not recommended, it's better to use master-slave config where slave used as failover), for other database especially RDBMS that persist to disk pretty sure can only do ~50K tps, Clickhouse can be multi-master, and last time i check, it can do ~600K inserts per seconds (while this time it's around 1M inserts per seconds), I simulate the atomic counter on Clickhouse using TimeStamp64Milli, the query limited to 100 queries per second but it's quite good enough for pub-sub use case. The benefit of using database as MQ/PubSub is that you can do a very flexible query (SQL support), mostly better tooling (especially Clickhouse), or update the record for new consumer, but the cons is that you must notify/fan-out (for example using NATS broadcast, only push the signal for worker to pull), track the ack/retries and the read offset of the workers yourself (pull).

Alternative Strategy for Dependency Injection (lambda-returning vs function-pointer)

There's some common strategy for injecting dependency (one or sets of function) using interface, something like this:

type Foo interface{ Bla() string }
type RealAyaya struct {}
func(a *RealAyaya) Bla() {}

type MockAyaya struct {} // generated from gomock or others

func(a *MockAyaya) Bla() {}
// real usage:

deps := RealAyaya{}

deps.Bla()

// test usage:

deps := MockAyaya{}

deps.Bla()

and there's another one (dependency parameter on function returning a lambda):

type Bla func() string

type DepsIface interface { ... }
func NewBla(deps DepsIface) Bla {
  return func() string {
    // do something with deps
  }
} 

// real usage:

bla := NewBla(realDeps)

res := bla()

// test usage:

bla := NewBLa(mockedOrFakeDeps)

res := bla()

and there other way by combining both fake and real implementation like this, or alternatively using proxy/cache+codegen if it's for 3rd party dependency.

and there other way (plugggable per-function level):

type Bla func() string
type BlaCaller struct {

  BlaFunc Bla
}
// real usage:

bla := BlaCaller{ BlaFunc: deps.SomeMethod }
res := bla.BlaFunc()

// test usage:

bla := BlaCaller{ BlaFunc: func() string { return `fake` } }
res := bla.BlaFunc()

Analysis

The first one is the most popular way, the 2nd one is one that I saw recently (that also being used in openApi/swagger codegen, i forgot which library), the bad part is that we have to sanitize the trace manually because it would show something like NewBla.func1 in the traces, and we have to use generated mock or implement everything if we have to test. Last style is what I thought when writing some task, where the specs still unclear whether I should:
1. query from local database

2. hit another service

3. or just a fake data (in the tests)

I can easily switch out any function without have to depend on whole struct or interface, and it would be still easy to debug (set breakpoint) and jump around the method, compared to generated mock or interface version.

Probably the bad part is, we have to inject every function one by one for each function that we want to call (which nearly equal effort as the 2nd one). But if that's the case, when your function requires 10+ other function to inject, maybe it's time to refactor?

The real use case would be something like this:

type LoanExpirationFunc func(userId string) time.Time 

type InProcess1 struct {
  UserId string // add more input here

  LoanExpirationFunc LoanExpirationFunc

  // add more injectable-function, eg. 3rd party hit or db read/save
}

type OutProcess1 struct {}
func Process1(in *InProcess1) (out *OutProcess1) {

  if ... // eg. validation

  x := in.LoanExpirationFunc(in.UserId) 
  // ... // do something
}

func defaultLoanExpirationFunc(userId string) time.Time {
  // eg. query from database

}

type thirdParty struct {} // to put dependencies
func NewThirdParty() (*thirdParty) { return &thirdParty{} }
func (t *thirdParty) extLoanExpirationFunc(userId string) time.Time {

  // eg. hit another service
}

// init input:

func main() {
  http.HandleFunc("/case1", func(w, r ...) {
    in := InProcess1{LoanExpirationFunc: defaultLoanExpirationFunc}
    in.ParseFromRequest(r)

    out := Process1(in)  

    out.WriteToResponse(w)
  })
  tp := NewThirdParty()

  http.HandleFunc("/case2", func(w, r ...) {
    in := InProcess1{LoanExpirationFunc: tp.extLoanExpirationFunc}
    in.ParseFromRequest(r)

    out := Process1(in)  

    out.WriteToResponse(w)
  })

}

// on test:

func TestProcess1(t *testing.T) {

  t.Run(`test one year from now`, func(t *testing.T) {
    in := inProcess1{LoanExpirationFunc: func(string) { return time.Now().Add(1, 0, 0) }}

    out := Process1(in)

    assert.Equal(t, out, ...)
  })

}

Haven't using this strategy extensively on new a project (since I just thought about this today and yesterday when creating horrid integration test), but I'll update this post when I found annoyance with this strategy.

 

UPDATE 2022: after using this strategy extensively for a while, this one is better than interface (especially when using IntelliJ), my tip: it would be better if you use function pointer name and injected function name with same name.