Programming Posts

Changing Couchbase Data Directory Location

Usually Couchbase put its data on default location (/opt/couchbase/var/lib/couchbase/data). But because my disk’s space is full, i have to move Couchbase data to other location. In my case, /data folder which is mounted to an empty 100GB SSD disk. Below is how i change it,

First, i have to change the content of below file,

vi /opt/couchbase/etc/couchdb/default.d/capi.ini 

Changing database_dir and view_index_dir on below Couchbase configuration file, to my new location folder

max_dbs_open = 10000
database_dir = /data
view_index_dir = /data

db_frontend = capi_frontend
ip4_bind_address =
ip6_bind_address = ::
port = 8092

Next is move the content of old folder (/opt/couchbase/var/lib/couchbase/data), to our new folder (/data). And restart our Couchbase service to implement our changes.

  sudo service couchbase-server stop
  sudo service couchbase-server start

We can see the result on our Couchbase web console, hopefully it helps (H)

ps. My Couchbase version is “Enterprise Edition 6.0.0 build 1693”.


[Python] Fixing Fatal Error when Installing Mysqlclient with Pip

On my current project, im using Python, Django and Pip. But there is some issue when installing libraries needed for running the app,

pip install -r requirements.txt

Below is the error generated when running it,

_mysql.c:29:10: fatal error: 'my_config.h' file not found
   #include "my_config.h"
   1 error generated.
   error: command 'clang' failed with exit status 1
 Rolling back uninstall of mysqlclient

Looks like error happen when trying to install below library,


Below is Python and Pip version that im using,

$ python --version
Python 3.4.3
$ pip -V
pip 18.1 from /Users/m/.pyenv/versions/3.4.3/envs/logistic_backend/lib/python3.4/site-packages/pip (python 3.4)

The main culprit is corresponding library is not supported by Python 3.4.3, changing it into below lib fix the error


Go and DataDog Part 1 : Simple Go HTTP Monitoring with DataDog

Today im trying to share a simple GoLang HTTP monitoring using DataDog APM. On this example im using trial version of DataDog for testing purpose, but on production im using enterprise version. Shouldnt be so much difference between those two.

Okay, after we register on DataDog website, we can start with installing DataDog agent on our server.

Im using Ubuntu Server, so my installation command is like below,

DD_API_KEY=<your datadog api key> bash -c "$(curl -L"

After our installation runs well, we need to configure APM first

vi /etc/datadog-agent/datadog.yaml

with below configuration,

   enabled: true

we can start our Datadog-agent using below command,

sudo datadog-agent start

and check its status using

sudo datadog-agent status

Next is writing our code, im using Go’s simple htttp package

package main

import (
    httptrace "" // In order to instrument the http library

func main() {
     defer tracer.Stop()
     mux := httptrace.NewServeMux() // init the http tracer
     mux.HandleFunc("/", handle) // use the tracer to handle the urls     
     log.Println("Listening on port 8099")	 
     log.Fatal(http.ListenAndServe(":8099", mux)) // use the tracer to start the http server

func handle(w http.ResponseWriter, r *http.Request) {     
     fmt.Fprint(w, "Hello world!")

we can simulate http request to our server with a simple command,

curl -v

We can see the result on our Datadog dashboard below,

and the detailed view for each url,

Hopefully it helps :-D


How to do a Simple Query Analysis and Optimization in Couchbase

I’ve joined an Indonesian Startup named Mapan since this September, leaving my previous role at a multinational consulting company. And to be honest, in here there are so many things to learn and a whole new playground to play with, and let me share one of new thing i’ve learn during my one month service here.

After im spending some time with mandatory company induction and way of working, i started with some technical matters and found a quite interesting issue regarding slow queries to Couchbase database, thats why im share step by step on how to analyze and gives workarond so that perhaps it wont happen again.

Dislaimer : on the time this writing is created, im working with Couchbase for less than 1 month. This writing is still open for discussion and improvement :-D .

Study Case #1

SELECT inventory.*
FROM inventory
WHERE inventory.`key` LIKE "ih:RUMA_0101_AABY12349%"
limit 1000

With almost 1 million data, it took more than 30seconds to fetch 1000 data which have key that starts with “ih:RUMA_0101_AABY12349”.

First analysis is that corresponding query despite hitting on an index, it still gives a quite amount of time of fetching as you can see on below image. It is happen mostly due to fetching is done first with all the data (85% of time is used for this, around 38 seconds) and do the filtering later on.

First thing that i see is that query is done on “key” field which have a common format, like “key”: “ih:RUMA_0101_AABY12349:1534087762346131”. So, instead of searching based on wildcard, better do searching on split string of “key” field, and create and index based on it.

CREATE INDEX key02_split ON inventory ( split(inventory.`key`, ":")[1] )   
PARTITION BY HASH (split(inventory.`key`, ":")[1]) where split(inventory.`key`, ":")[1] is not null

And change the query needed into below,

SELECT inventory.*
FROM inventory
WHERE split(inventory.`key`, ":")[1] = 'RUMA_0101_AABY12349'
limit 1000

Drastically reducing the query time into 80ms, as on below screenshot.

Study Case #2

SELECT `inventory_trx`.* FROM `inventory_trx` 
WHERE client = "RUMA" 
and movement_type in ["IB001", "IB002", "IB003", "OB001", "OB002", "TF001", "TF002", 
"IA001", "IA003", "IA005", "IA007", "IA002", "IA004"] 
and transaction_timestamp between 1525107600 and 1537808399 and  
(origin.code = "0101" or destination.code = "0101")  
order by transaction_timestamp desc offset 0 limit 1000

Again, same issue happen here. Despite hitting get_stock_mutation index, it still takes some time for fetching. On below screenshot, it shows almost 36 seconds only for fetching.

Again, almost the same approach is used for this case. We can see which field is used on WHERE parameter, and start indexing it. The only difference is that it have range query on transaction_timestamp field and also order by criteria, which will make it a little bit complicated.

It took not only query optimization, but also some negotiation to implements some changes on User Interface. On previous query, transaction_timestamp parameter doesnt have any limitation on start and end date, therefore make it harder to do indexing. After consulting with user, they are agree that there should be a one month limitation for doing range query.

After user agree with changes on UI, next is to create an Index on Couchbase for corresponding query.

CREATE INDEX inventory_trx_ix01 
ON inventory_trx ( client, movement_type, DATE_PART_MILLIS(transaction_timestamp * 1000, "month"), 
origin.code, destination.code, DATE_PART_MILLIS(transaction_timestamp * 1000, "year"))

Tweaking the query into below,

SELECT `inventory_trx`.* FROM `inventory_trx` 
WHERE client = "RUMA" 
and movement_type in ["IB001", "IB002", "IB003", "OB001", "OB002", 
"TF001", "TF002", "IA001", "IA003", "IA005", "IA007", "IA002", "IA004"] 
and DATE_PART_MILLIS(transaction_timestamp * 1000, "month") = 9
and DATE_PART_MILLIS(transaction_timestamp * 1000, "year") = 2018
and  (origin.code = "0101" or destination.code = "0101")
limit 1000

Reducing query time needed significantly,


How to Convert from a Point to a KML files and Generate a Circle with Radius on Each Point

Yesterday I got a challenging assignment to generate a telco network coverage map. But the only data i got is BTS (Base Transceiver Station) location, which is only latitude, longitude and coverage radius given on excel file. I need to convert those excel sheet data into a google map page, and displayed it to subscribers accordingly.

The biggest problem that i had is the number of data is quite big, i need to map more than 20k BTS location and need displayed it fast and without lagging. So a simple google maps script wont work because google maps have a limitation for this.

So i need to create a workaround, and after researching for quite some times i pick KML and Google FusionTable. KML data format for creating radius, and Google FusionTable for displaying it.

This is my PHP code for converting latitude, longitude and radius into KML code.

$lats = array('4.53191944444444','5.34593888888889');
$longs = array('97.93600000000001','95.99299999999999');
$meter = 2000; 
$kml = '<?xml version="1.0" encoding="UTF-8"?><kml xmlns="" xmlns:atom=""><Document>        
            <Style id="style0">

for($i = 0; $i < count($lats); $i++) {

    $lat = $lats[$i];
    $long = $longs[$i];

    // Get circle coordinates
    $coordinatesList = getCirclecoordinates($lat, $long, $meter);

    // Output
    $kml  .= '<Placemark><name>Circle '.$i.'</name><styleUrl>#style0</styleUrl><Polygon><outerBoundaryIs><LinearRing><coordinates>'.$coordinatesList.'</coordinates></LinearRing></outerBoundaryIs></Polygon></Placemark>';

$kml .= '</Document></kml>';
function getCirclecoordinates($lat, $long, $meter) {
  // convert coordinates to radians
  $lat1 = deg2rad($lat);
  $long1 = deg2rad($long);
  $d_rad = $meter/6378137;
  $coordinatesList = "";
  // loop through the array and write path linestrings
  for($i=0; $i<=360; $i+=3) {
    $radial = deg2rad($i);
    $lat_rad = asin(sin($lat1)*cos($d_rad) + cos($lat1)*sin($d_rad)*cos($radial));
    $dlon_rad = atan2(sin($radial)*sin($d_rad)*cos($lat1), cos($d_rad)-sin($lat1)*sin($lat_rad));
    $lon_rad = fmod(($long1+$dlon_rad + M_PI), 2*M_PI) - M_PI;
    $coordinatesList .= rad2deg($lon_rad).",".rad2deg($lat_rad).",0 ";
  return $coordinatesList;

$fp = fopen('e:\lele.kml', 'w');
fwrite($fp, $kml);

After KML is done, i just need to upload it to Google FusionTable and display it on my web page.