skyrocknroll

• https://www.k5worksheets.com/
• http://printables.atozteacherstuff.com/

Quizzes

• https://quizlet.com/

Coloring

• https://www.coloring.ws/coloring.html

skyrocknroll

If i refer to the doc, vmagent stores the data in a buffer reference by "-remoteWrite.tmpDataPath". Am I right?
	Correct.
There for where are the data collected by vmagent stored when vmstorage doesn't respond?
	If vminsert fails to send data to a particular vmstorage node, then it falls back by sending the data to the remaining vmstorage nodes. This prevents from data loss on ingestion path. If all the vmstorage nodes are unavailable, then vminsert starts buffering incoming data in memory in the hope vmstorage nodes will appear soon. The buffer size is limited by -memory.allowedPercent command-line flag. When the buffer becomes full, then vminsert starts dropping newly incoming data and responding with 503 Service Unavailable status code. Conforming clients such as vmagent must retry sending the data if they receive responses with such status code.

• https://victoriametrics.slack.com/archives/CGZF1H6L9/p1595268389008100?thread_ts=1594813577.444400&cid=CGZF1H6L9

For converting all the hyphens to u

skyrocknroll

• My Router Archer c50 eu v1

• THe firmware version was 160411.

• OpenWrt page is https://openwrt.org/toh/tp-link/archer-c50?datasrt=firmware%20openwrt%20upgrade%20url#v1_and_v3_installation_instructions

• Downlaoded followinf two files

  • http://downloads.openwrt.org/releases/18.06.0/targets/ramips/mt7620/openwrt-18.06.0-ramips-mt7620-ArcherC50v1-squashfs-factory-eu.bin
  • http://downloads.openwrt.org/releases/19.07.2/targets/ramips/mt7620/openwrt-19.07.2-ramips-mt7620-ArcherC50v1-squashfs-sysupgrade.bin

• from factory firmware upgrade using factory-eu.bin

• Uploaded factory-eu.bin in TP link upgrade UI. It brick the router :(

• We need to downgrade the router to 150325 version.

skyrocknroll

# a 5 minute disk thrashing benchmark
# generates equal amounts of random read and write IO on every drive
# will generate metrics for each drive
[global]
ioengine=libaio
direct=1
unified_rw_reporting=1
rw=randrw
time_based=1
runtime=300s

skyrocknroll

#! /bin/bash
# Make sure you grab the latest version
curl -OL https://github.com/google/protobuf/releases/download/v3.6.1/protoc-3.6.1-linux-x86_64.zip
https://github.com/google/protobuf/releases/download/v3.6.1/protoc-3.6.1-linux-x86_64.zip
# Unzip
unzip protoc-3.6.1-linux-x86_64.zip -d protoc3

# Move protoc to /usr/local/bin/
sudo mv protoc3/bin/* /usr/local/bin/

skyrocknroll

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

skyrocknroll

package main

//#include<stdio.h>
//void inC() {
//    printf("I am in C code now!\n");
//}
import "C"
import "fmt"

func main() {

skyrocknroll

* install the latest fdb client
* go mod init fdblearn
* go get github.com/apple/foundationdb/bindings/[email protected]
* make sure you set apiVersion to <= 600
* because the client library installed is 6.0.18 which is 600. 610 can be used only when 6.1.x client libraries are installed
* invalidate the cache and restart if ide complains.

skyrocknroll

High Performance Redis

In response to this brief blog entry, @antirez tweeted for some documentation on high-performance techniques for Redis. What I present here are general high-performance computing (HPC) techniques. The examples are oriented to Redis. but they work well for any program designed to be single- or worker-threaded and asynchronous (e.g. uses epoll).

The motivation for using these techniques is to maximize performance of our system and services. By isolating work, controlling memory, and other tuning, you can achieve significant reduction in latency and increase in throughput.

My perspective comes from the microcosm of my own bare-metal (vs VM), on-premises deployment. It might not be suitable for all scenarios, especially cloud deployments, as I have little experience with HPC there. After some discussion, maybe this can be adapted as [redis.io documentation](https://redis.io/do

skyrocknroll

import pulsar
from pulsar import MessageId

client = pulsar.Client('pulsar://pulsar.stage.x.com:6650',)

consumer = client.subscribe('persistent://public/default/my-topic-3', 'test-sub5',)
consumer.seek(pulsar.MessageId.earliest)
#consumer.seek(pulsar.MessageId.latest)
while True: