Jules Ivanic guizmaii

The default formula use by AWS RDS to calculate the max_connections parameter is: LEAST({DBInstanceClassMemory/9531392},5000)

But It's hard to find the exact value of DBInstanceClassMemory.

So, here are the values I got when I ran the SQL commmand: show max_connections; in some RDS instances:

Instance type	RAM (GB)	max_connections
db.t2.small	2	198
db.t2.medium	4	413

AWS (client) => GCE (server) via VPN:

logs clients:

ubuntu@ip-172-31-43-143:~$ iperf -c 10.132.0.2 -f M -p 5001
------------------------------------------------------------
Client connecting to 10.132.0.2, TCP port 5001
TCP window size: 0.04 MByte (default)
------------------------------------------------------------
[  3] local 172.31.43.143 port 55156 connected with 10.132.0.2 port 5001

Quick Tips for Fast Code on the JVM

I was talking to a coworker recently about general techniques that almost always form the core of any effort to write very fast, down-to-the-metal hot path code on the JVM, and they pointed out that there really isn't a particularly good place to go for this information. It occurred to me that, really, I had more or less picked up all of it by word of mouth and experience, and there just aren't any good reference sources on the topic. So… here's my word of mouth.

This is by no means a comprehensive gist. It's also important to understand that the techniques that I outline in here are not 100% absolute either. Performance on the JVM is an incredibly complicated subject, and while there are rules that almost always hold true, the "almost" remains very salient. Also, for many or even most applications, there will be other techniques that I'm not mentioning which will have a greater impact. JMH, Java Flight Recorder, and a good profiler are your very best friend! Mea

	Latency Comparison Numbers
	--------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

	import com.vividsolutions.jts.geom.Geometry
	import com.vividsolutions.jts.io.ParseException
	import com.vividsolutions.jts.io.geojson.{GeoJsonReader, GeoJsonWriter}
	import play.api.libs.json._

	import scala.reflect.ClassTag
	import scala.util.{Failure, Success, Try}

	object GeometryJson {
	implicit final val geometryFormat: Format[Geometry] = {

	import cats.data.NonEmptyList
	import cats.Eq

	import scala.collection.GenTraversableOnce

	sealed trait NonEmptySet[T] extends (T => Boolean) {
	def head: T
	def set: Set[T]
	def +(t: T): NonEmptySet[T]
	def ++(ts: NonEmptySet[T]): NonEmptySet[T]

	import cats.data.NonEmptyList
	import cats.Eq

	import scala.collection.GenTraversableOnce

	sealed trait NonEmptySet[T] extends (T => Boolean) {
	def head: T
	def set: Set[T]
	def +(t: T): NonEmptySet[T]
	def ++(ts: NonEmptySet[T]): NonEmptySet[T]

	@ import $ivy.`org.typelevel::cats-effect:0.10.1`

	@ import cats._, cats.data._, cats.implicits._, cats.effect._

	@ def lift(i: Int): IO[Int] = Timer[IO].sleep(1.second) >> IO {

	println(Thread.currentThread.getName + "At time = " + java.time.Instant.now)
	i
	}

	import com.vividsolutions.jts.geom.{GeometryFactory, PrecisionModel}

	object GeometryFactoryInstance {

	lazy val instance: GeometryFactory = new GeometryFactory(new PrecisionModel(), 4326)

	}