Skip to main content

Storing Request Statistics Using Redis

If we build a backend service, we probably need to keep any statistics related to our service such as number of requests, average time for processing, number of errors, etc. We may also need to have statistic records in several time precisions for example hourly and daily. The simplest solution, we can just store any information into a table in our database, then calculate the summary when needed or by a request. But, it will cost our system storage and computing resources.

Unlike relational databases or NoSQL that only have CRUD operations in general, Redis operations are related to the type of data that is stored. For example, a list will have push or pop operations, a sorted set will have a ranking, incrementing, or union operation, and so on. For instance for storing request statistics in Redis, we will store statistics of the response time of a backend application. The metrics are minimum time, maximum time, number of responses, and total response time. The last two metrics can be used for calculating the average time to respond. We will also store the statistics for hourly and daily information.

We need several records in sorted sets to store the following information.

  • List of starting times of all periods
  • Metrics of each period
  • Temporary data for aggregating minimum and maximum values

A list of starting times is used to help us to list all metrics records that we have. The record of metrics of each period will be named with starting time of the period.

Our backend application will call a function every time it handles a request with a response time value in milliseconds as the first parameter of the function.

All metrics in a period will be aggregated in a single record directly. Every time the function is called, it will calculate the period based on the calling time and the precision value.

The processes are as follows.

  1. Calculate starting time of current period
  2. Store the response time value in temporary records as both minimum and maximum values of response time
  3. Aggregate data in temporary records with matrics record of the current period
  4. Increment number of responses and total response time in the matrics record of the current period

For this example, we utilize Node.js with the redis module. We also need uuid for generating a random key for temporary records. The implementation is as follows.

function captureStats(responseTime) {
  const now = Date.now();
  const hourly = 3600 * 1000;
  const daily = 24 * 3600 * 1000;
  
  const tempKeyMin = `stats:temp:${uuidv1()}`;
  const tempKeyMax = `stats:temp:${uuidv1()}`;

  // get starting time of the period
  const stimeHourly = Math.floor(now / hourly) * hourly;
  const stimeDaily = Math.floor(now / daily) * daily;
  
  // to store starting time of each period
  const timesHourlyKey = `stats:times:${hourly}`;
  const timesDailyKey = `stats:times:${daily}`;
  
  // to store metrics of each period
  const statsHourlyKey = `stats:${hourly}:${stimeHourly}`; 
  const statsDailyKey = `stats:${daily}:${stimeDaily}`; 

  redisClient.multi()
    .zadd(timesHourlyKey, now, stimeHourly)
    .zadd(timesDailyKey, now, stimeDaily)
    .zadd(tempKeyMin, responseTime, 'min')
    .zadd(tempKeyMax, responseTime, 'max')
    .zunionstore(statsHourlyKey, 2, statsHourlyKey, tempKeyMin, 'aggregate', 'min')
    .zunionstore(statsHourlyKey, 2, statsHourlyKey, tempKeyMax, 'aggregate', 'max')
    .zunionstore(statsDailyKey, 2, statsDailyKey, tempKeyMin, 'aggregate', 'min')
    .zunionstore(statsDailyKey, 2, statsDailyKey, tempKeyMax, 'aggregate', 'max')
    .zincrby(statsHourlyKey, 1, 'count')
    .zincrby(statsDailyKey, 1, 'count')
    .zincrby(statsHourlyKey, responseTime, 'sum')
    .zincrby(statsDailyKey, responseTime, 'sum')
    .expire(tempKeyMin, 10)
    .expire(tempKeyMax, 10)
    .exec((err, results) => {
      // check
    });

}

Each metric record will have the following parameters.

  • min. The value will be updated by aggregating the latest value in the temporary record with the current value in the metric record using zunionstore and the aggregate min method.
  • max. The value will be updated by aggregating the latest value in the temporary record with the current value in the metric record using zunionstore and the aggregate max method.
  • count. The value will be incremented by one in each function call.
  • sum. The value will be incremented by the response time value in each function call.

Comments

Popular posts from this blog

Configuring Swap Memory on Ubuntu Using Ansible

If we maintain a Linux machine with a low memory capacity while we are required to run an application with high memory consumption, enabling swap memory is an option. Ansible can be utilized as a helper tool to automate the creation of swap memory. A swap file can be allocated in the available storage of the machine. The swap file then can be assigned as a swap memory. Firstly, we should prepare the inventory file. The following snippet is an example, you must provide your own configuration. [server] 192.168.1.2 [server:vars] ansible_user=root ansible_ssh_private_key_file=~/.ssh/id_rsa Secondly, we need to prepare the task file that contains not only the tasks but also some variables and connection information. For instance, we set /swapfile  as the name of our swap file. We also set the swap memory size to 2GB and the swappiness level to 60. - hosts: server become: true vars: swap_vars: size: 2G swappiness: 60 For simplicity, we only check the exi

Rangkaian Sensor Infrared dengan Photo Dioda

Keunggulan photodioda dibandingkan LDR adalah photodioda lebih tidak rentan terhadap noise karena hanya menerima sinar infrared, sedangkan LDR menerima seluruh cahaya yang ada termasuk infrared. Rangkaian yang akan kita gunakan adalah seperti gambar di bawah ini. Pada saat intensitas Infrared yang diterima Photodiode besar maka tahanan Photodiode menjadi kecil, sedangkan jika intensitas Infrared yang diterima Photodiode kecil maka tahanan yang dimiliki photodiode besar. Jika  tahanan photodiode kecil  maka tegangan  V- akan kecil . Misal tahanan photodiode mengecil menjadi 10kOhm. Maka dengan teorema pembagi tegangan: V- = Rrx/(Rrx + R2) x Vcc V- = 10 / (10+10) x Vcc V- = (1/2) x 5 Volt V- = 2.5 Volt Sedangkan jika  tahanan photodiode besar  maka tegangan  V- akan besar  (mendekati nilai Vcc). Misal tahanan photodiode menjadi 150kOhm. Maka dengan teorema pembagi tegangan: V- = Rrx/(Rrx + R2) x Vcc V- = 150 / (150+10) x Vcc V- = (150/160) x 5

Installing VSCode Server Manually on Ubuntu

I've ever gotten stuck on updating the VSCode server on my remote server because of an unstable connection between my remote server and visualstudio.com that host the updated server source codes. The download and update process failed over and over so I couldn't remotely access my remote files through VSCode. The solution is by downloading the server source codes through a host with a stable connection which in my case I downloaded from a cloud VPS server. Then I transfer the downloaded source codes as a compressed file to my remote server through SCP. Once the file had been on my remote sever, I extracted them and align the configuration. The more detailed steps are as follows. First, we should get the commit ID of our current VSCode application by clicking on the About option on the Help menu. The commit ID is a hexadecimal number like  92da9481c0904c6adfe372c12da3b7748d74bdcb . Then we can download the compressed server source codes as a single file from the host.

Resize VirtualBox LVM Storage

VirtualBox is a free solution to host virtual machines on your computer. It provides configuration options for many components on our machine such as memory, storage, networking, etc. It also allows us to resize our machine storage after its operating system is installed. LVM is a volume manager in a Linux platform that helps us to allocate partitions in the system and configure the storage size that will be utilized for a specific volume group. There are some points to be noticed when we work with LVM on VirtualBox to resize our storage. These are some steps that need to be performed. 1. Stop your machine before resizing the storage. 2. Set new storage size using GUI by selecting " File > Virtual Media Manager > Properties " then find the desired virtual hard disk name that will be resized. OR , by running a CLI program located in " Program Files\Oracle\VirtualBox\VBoxManage.exe ".  cd "/c/Program Files/Oracle/VirtualBox" ./VBoxManage.exe list

Managing MongoDB Records Using NestJS and Mongoose

NestJS is a framework for developing Node.js-based applications. It provides an additional abstraction layer on top of Express or other HTTP handlers and gives developers a stable foundation to build applications with structured procedures. Meanwhile, Mongoose is a schema modeling helper based on Node.js for MongoDB. There are several main steps to be performed for allowing our program to handle MongoDB records. First, we need to add the dependencies which are @nestjs/mongoose , mongoose , and @types/mongoose . Then, we need to define the connection configuration on the application module decorator. import { MongooseModule } from '@nestjs/mongoose'; @Module({ imports: [ MongooseModule.forRoot('mongodb://localhost:27017/mydb'), ], controllers: [AppController], providers: [AppService], }) Next, we create the schema definition using helpers provided by NestJS and Mongoose. The following snippet is an example with a declaration of index setting and an o

Generate API Documentation Using Swagger Module in NestJS

Swagger provides us a standard to generate API documentation based on the Open API specification. If we use NestJS for building our API providers, we can utilize a tool provided by NestJS in the  @nestjs/swagger  module to generate the documentation automatically in the built time. This module also requires the swagger-ui-express module if we use Express as the NestJS base HTTP handler. Set Swagger configuration First, we need to define Swagger options and instantiate the documentation provider on the main.ts file. import { DocumentBuilder, SwaggerModule } from '@nestjs/swagger'; // sample application instance const app = await NestFactory.create(AppModule); // setup Swagger options const options = new DocumentBuilder() .setTitle('Coffee') .setVersion('1.0') .setDescription('Learn NestJS with coffee') .build(); // build the document const document = SwaggerModule.createDocument(app, options); // provide an endpoint