dvliman

-- show running queries (pre 9.2)
SELECT procpid, age(query_start, clock_timestamp()), usename, current_query 
FROM pg_stat_activity 
WHERE current_query != '<IDLE>' AND current_query NOT ILIKE '%pg_stat_activity%' 
ORDER BY query_start desc;

-- show running queries (9.2)
SELECT pid, age(query_start, clock_timestamp()), usename, query 
FROM pg_stat_activity 
WHERE query != '<IDLE>' AND query NOT ILIKE '%pg_stat_activity%' 

dvliman

package main

import (
	"context"
	"flag"
	"fmt"
	"log"
	"net/http"
	"os"
	"os/signal"

dvliman

(ns your.namespace.async-util
    (:require
      [clojure.core.async :as a :refer [>! <! >!! <!! go chan buffer
                                        close! thread alts! alts!! timeout]]
      [clojure.core.match :as match :refer [match]]))

;;
;; core.async util fns
;;

dvliman

Useful Spacemacs commands

• SPC q q - quit
• SPC w / - split window vertically
• SPC w - - split window horizontally
• SPC 1 - switch to window 1
• SPC 2 - switch to window 2
• SPC w c - delete current window
• SPC TAB - switch to previous buffer
• SPC b b - switch buffers

dvliman

Reader Macros in Common Lisp

This post also appears on lisper.in.

Reader macros are perhaps not as famous as ordinary macros. While macros are a great way to create your own DSL, reader macros provide even greater flexibility by allowing you to create entirely new syntax on top of Lisp.

Paul Graham explains them very well in [On Lisp][] (Chapter 17, Read-Macros):

The three big moments in a Lisp expression's life are read-time, compile-time, and runtime. Functions are in control at runtime. Macros give us a chance to perform transformations on programs at compile-time. ...read-macros... do their work at read-time.

dvliman

Client-side SSL

For excessively paranoid client authentication.

---

Updated Apr 5 2019:

because this is a gist from 2011 that people stumble into and maybe you should AES instead of 3DES in the year of our lord 2019.

some other notes:

dvliman

Turning PostgreSQL into a queue serving 10,000 jobs per second

RDBMS-based job queues have been criticized recently for being unable to handle heavy loads. And they deserve it, to some extent, because the queries used to safely lock a job have been pretty hairy. SELECT FOR UPDATE followed by an UPDATE works fine at first, but then you add more workers, and each is trying to SELECT FOR UPDATE the same row (and maybe throwing NOWAIT in there, then catching the errors and retrying), and things slow down.

On top of that, they have to actually update the row to mark it as locked, so the rest of your workers are sitting there waiting while one of them propagates its lock to disk (and the disks of however many servers you're replicating to). QueueClassic got some mileage out of the novel idea of randomly picking a row near the front of the queue to lock, but I can't still seem to get more than an an extra few hundred jobs per second out of it under heavy load.

So, many developers have started going straight t

dvliman

Spike

I looked into the state of GraalVM and Clojure and wrote some small work-related scripts.

GraalVM Build Tools

• Downloaded GraalVM and set $GRAALVM_HOME
• Found two main libraries for building GraalVM CLIs - https://github.com/luchiniatwork/cambada#packaging-as-a-native-image and https://github.com/taylorwood/clj.native-image (or https://github.com/taylorwood/lein-native-image for the lein equivalent). I chose clj.native-image as it seemed more focused on doing one thing well.
• First tried its template example which was easy to follow.
• Then added a graalvm build to one of our small cli tools
• Found it to be pretty straightforward except for a stdout flushing issue that was trivial to fix

dvliman

# Turn on the simulator screen capture
xcrun simctl io booted recordVideo ~/simulator.mov

# Convert the iPhone 6s screen shot into a gif:
ffmpeg -i ~/simulator.mov -vf scale=320:-1 -r 6 -f gif -y simulator.gif

dvliman

import os
import pickle
import warnings

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout