How Badoo saved one million dollars switching to PHP7 By Badoo on 14 Mar 2016 - 9 Comments PHP Introduction adjust the app cluster processing load more than justifies itself from the economic standpoint (by a million With over three million lines of PHP code and 60,000 tests, this project took on epic proportions. came up with a new PHP app testing framework (which, by the way, is already open source), and saved a million 字数有限制，原文： https://techblog.badoo.com/blog/2016/03/14/how-badoo-saved-one-million-dollars-switching-to-php7
这是关于学习使用Unity的基础知识的系列文章中的第五篇。这次，我们将使用计算着色器显著提高图形的分辨率。
2核2G云服务器 每月9.33元起，个人开发者专属3年机 低至2.3折
ENSG00000237235 TRDD2 TR_D_gene 9 ENSG00000223997 TRDD1 TR_D_gene 8 可变剪接调控基因RBFOX1以2.7 million CTBP2P1和CCNQP2相距最远，距离有30个million。这是两个pseudogene。
China – Shenzhen Catic Real Estate Co Ltd<000043> announced today that it plans to issue up to 260 million CATIC International Holdings Ltd<0232> will subscribe up to 120 million shares for RMB 1 billion in cash In Addition, Shenzhen Catic Real Estate will issue up to 140 million shares worth RMB 1.2 billion to and half to repay a loan of RMB 600 million. RMB 700 million of the proceeds will be used for the development of three properties, and RMB 300 million
要做到这一点，你需要开始使用两个表： one_million 和 half_million 来做一些示例。 ANALYZE one_million; EXPLAIN SELECT * FROM one_million; QUERY PLAN __________________________________ 还有一些其它算法的示例： EXPLAIN ANALYZE SELECT * FROM one_million JOIN half_million ON (one_million.counter=half_million.counter ANALYZE SELECT * FROM one_million JOIN half_million ON (one_million.counter=half_million.counter); QUERY = half_million.counter) -> Index Scan using one_million_counter_idx on one_million (cost=0.00
Processed 91.01 million rows, 728.06 MB (375.91 million rows/s., 3.01 GB/s.) Processed 7.75 million rows, 61.96 MB (191.44 million rows/s., 1.53 GB/s.) Processed 600.04 million rows, 6.20 GB (70.11 million rows/s., 725.04 MB/s.) Processed 600.04 million rows, 5.60 GB (482.97 million rows/s., 4.51 GB/s.) Processed 546.67 million rows, 5.48 GB (154.72 million rows/s., 1.55 GB/s.)
reads，并将该数字除以1,000,000——这是我们的“每百万”缩放因子 ( “per million” scaling factor ) 。 消除测序深度影响，得到每百万reads（RPM, reads per million ） 将RPM值除以基因长度（以千碱基为单位），消除基因长度影响，得到RPKM。 FPKM FPKM (Fragments Per Kilobase Million, or Fragments Per Kilobase of transcript per Million reads TPM TPM (Transcripts Per Million, or Transcripts Per kilobase of exon model per Million mapped reads) CPM CPM（Counts Per Million, or Counts of exon model Per Million mapped reads） 每百万映射读取的counts 除了RPKM、
Processed 91.01 million rows, 728.06 MB (375.91 million rows/s., 3.01 GB/s.) Processed 600.04 million rows, 6.20 GB (70.11 million rows/s., 725.04 MB/s.) Processed 600.04 million rows, 5.60 GB (482.97 million rows/s., 4.51 GB/s.) Processed 546.67 million rows, 5.48 GB (154.72 million rows/s., 1.55 GB/s.) Processed 546.67 million rows, 5.56 GB (546.59 million rows/s., 5.56 GB/s.)
continent, population FROM world Show the name for the countries that have a population of at least 200 million . 200 million is 200000000, there are eight zeros. Million：6个0 Billion：8个0 SELECT name FROM world WHERE population > 200000000; 关于人均GDP Give the name and sq km or it has a population of more than 250 million. Show the countries that are big by area (more than 3 million) or big by population (more than 250 million
read.csv(p,stringsAsFactors=FALSE) #这里读取的就是我们上面的population.csv names(mydata)[1]<-c("Country" ,"Scale" ,"million ","fan" ) #重新定义列明，并在原基础上新加了million和fan列 mydata$million<-mydata$Scale/1000000 mydata$fan<-cut(mydata $million, breaks=c(min(mydata$million,na.rm=TRUE), 0,300,600,900,1200,1500,1800,2100,2400 , max(mydata$million,na.rm=TRUE)), labels=c(" <=0","0~300"," "albers", parameters = c(0, 0))+ scale_y_continuous(breaks=(-3:3)*30) + scale_fill_manual(name="<em>million</em>
cat sample.fq | awk 'BEGIN{OFS="\t"}{if(FNR%4==0) base+=length}END{print FNR/4/1000000 " million", base /10^9 "G";}' # 3e-06 million 1.41e-07 G # 统计多个文件 for i in *.fq; do cat sample.fq | awk -v name=${ i} 'BEGIN{OFS="\t"}{if(FNR%4==0) base+=length}END{print name, FNR/4/1000000 " million", base/10^9 " G ";}' done # sample.fq 3e-06 million 1.41e-07 G # 统计多个压缩文件 for i in *.fq.gz; do zcat sample.fq.gz | awk -v name=${i} 'BEGIN{OFS="\t"}{if(FNR%4==0) base+=length}END{print name, FNR/4/1000000 " million
2 的次幂 2的次幂 英语里面常讲 1 个 Million，1 个 Billion，分别是百万、十亿的意思。可以看到，以 3 个 0 为一组，层层递进。 先给出了一些预设： 300 个 million 的月活跃用户 50% 的用户每天都使用 twitter 用户平均每天发表 2 条 tweets 10% 的 tweets 包含多媒体 多媒体数据保存 5 年 下面是估算的过程： 先预估 QPS： DAU（每天的活跃用户数，Daily Active Users）为：300 million（总用户数） * 50% = 150 million 发 tweets 再来估算存储容量： 假设多媒体的平均大小为 1MB，那么每天的存储容量为：150 million * 2 * 10% * 1MB = 30 TB。 最后这两个的估算过程是这样的： 300 个 million * 10%* 1MB，1 MB 其实就是 6 个 0，相当于 million 要进化 2 次：million -> billion -> trillion
= 1000000 // 往切片中添加数据的长度，百万 SLICE_LENGTH_TEN_MILLION = 10000000 // 往切片中添加数据的长度，千万 SLICE_LENGTH_HUNDRED_MILLION BenchmarkNewSliceWithCap(b *testing.B) { for n := 0; n < b.N; n++ { newSliceWithCap(SLICE_LENGTH_MILLION , b) } func BenchmarkNewSlicTenMillion(b *testing.B) { testNewSlice(SLICE_LENGTH_TEN_MILLION, b) } func BenchmarkNewSlicHundredMillion(b *testing.B) { testNewSlice(SLICE_LENGTH_HUNDRED_MILLION, b) } 执行以下命令测试，只会匹配到上面新增的三个测试方法 go test -bench='Million$' -benchmem .
runs the fifth-largest stock brokerage in Indonesia by number of trades, announced it has raised a $25 million Piyajomkwan, Ajaib Group focuses on millennials and first-time investors, and currently claims one million It has now raised a total of $27 million, including a $2 million seed round in 2019. Stock investment has a very low penetration rate in Indonesia, with only about 1.6 million capital market Last week, Indonesian investment app Bibit announced a $30 million growth round led by Sequoia Capital
Processed 10.02 million rows, 80.18 MB (180.95 million rows/s., 1.45 GB/s.) Processed 10.02 million rows, 80.18 MB (630.94 million rows/s., 5.05 GB/s.) 对比上面上次查询的效率: 开启 JIT 是 (630.94 million rows/s., 5.05 GB/s.) 关闭 JIT 是 (180.95 million rows/s., 1.45 GB/s.) 两者相差 3.5 倍，提效确实很明显。
Processed 2.65 million rows, 81.69 MB (49.55 million rows/s., 1.53 GB/s.) Processed 83.72 million rows, 10.98 GB (31.45 million rows/s., 4.12 GB/s.) Processed 27.31 million rows, 2.14 GB (37.74 million rows/s., 2.95 GB/s.) Processed 25.48 million rows, 1.79 GB (41.68 million rows/s., 2.93 GB/s.) Processed 27.62 million rows, 3.21 GB (20.26 million rows/s., 2.35 GB/s.)
baozitraining/p/10945962.html B 站: https://www.bilibili.com/video/av53975943 Problem Statement In a 1 million by 1 million grid, the coordinates of each grid square are (x, y) with 0 <= x, y < 10^6. Assume boolean is 8 bit -> 1B, 1 Million * 1 Million = 1TB, OMG, immediately using a set instead. , ONE_MILLION, blockLookup, visited)) { 35 String nextKey = nextX + "," + nextY; , ONE_MILLION, blockLookup, visited)) { 54 String nextKey = nextX + "," + nextY;
company F5 announced today that it is acquiring Volterra, a multi-cloud management startup, for $500 million That breaks down to $440 million in cash and $60 million in deferred and unvested incentive compensation Volterra emerged in 2019 with a $50 million investment from multiple sources, including Khosla Ventures
Processed 16.38 thousand rows, 188.42 KB (2.60 million rows/s., 29.87 MB/s.) Processed 4.10 million rows, 56.53 MB (157.82 million rows/s., 2.17 GB/s.) Processed 50.00 million rows, 838.89 MB (334.89 million rows/s., 5.62 GB/s.) Processed 50.00 million rows, 589.10 MB (622.05 million rows/s., 7.33 GB/s.) 这是为什么呢？
csail.mit.edu/ github地址： https://github.com/metalbubble/places365 数据介绍：Places2 contains more than 10 million The train set of Places365-Standard has ~1.8 million images from 365 scene categories, where there are Places365-Standard and released them as below. * the train set of Places365-Challenge* has extra 6.2 million images along with all the images of Places365-Standard (so totally ~8 million images), where there
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