jboner

Latency Comparison Numbers (~2012)
----------------------------------
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

vasanthk

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

djspiewak

Thread Pools

Thread pools on the JVM should usually be divided into the following three categories:

1. CPU-bound
2. Blocking IO
3. Non-blocking IO polling

Each of these categories has a different optimal configuration and usage pattern.

hellerbarde

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

djspiewak

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

CMCDragonkai

Rust/Haskell: Higher-Kinded Types (HKT)

A higher kinded type is a concept that reifies a type constructor as an actual type.

A type constructor can be thought of in these analogies:

• like a function in the type universe
• as a type with a "hole" in it

LukaJCB

Uniting the bifunctor and monofunctor hierarchies

With the rise of so-called bifunctor IO types, such as ZIO, questions have naturally arisen of how one could leverage the cats-effect type classes to make use of this new power. So far suggestions have mostly focused on duplicating the existing hierarchy into two distinct branches, one parameterized over F[_] and another parameterized over F[_, _]. To me this is not a great situation, as now library maintainers would have to write code for both of these hierarchies or choose one and leave the other one in the dust.

Instead we should find a way to unite the two shapes under a single hierarchy. This is a draft on how to enable this unification using polymorphic function types in Dotty.

popravich

The standard names for indexes in PostgreSQL are:

{tablename}_{columnname(s)}_{suffix}

where the suffix is one of the following:

• pkey for a Primary Key constraint;
• key for a Unique constraint;
• excl for an Exclusion constraint;
• idx for any other kind of index;

ashrithr

Introduction to Kafka

Kafka acts as a kind of write-ahead log (WAL) that records messages to a persistent store (disk) and allows subscribers to read and apply these changes to their own stores in a system appropriate time-frame.

Terminology:

• Producers send messages to brokers
• Consumers read messages from brokers
• Messages are sent to a topic

smarter

Generalized Algebraic Data Types in Scala

Basic GADTs

Here's an ADT which is not a GADT, in Haskell:

data Expr = IntExpr Int | BoolExpr Bool