MonetDB

Intervals

INTERVAL data types allow us to conduct arithmetic calculations with temporal data types. Keyword DATE is casting operator. This operator is used so that Monetdb transform string to date data type.

SELECT DATE '2021-12-31' + INTERVAL '10' DAY;

It is possible to subtract two dates or times. Result will be interval in seconds.

SELECT date '2020-09-28'      - date '2020-09-27' AS "DifferenceInSeconds";	SELECT time '14:35:45'      - time '14:35:40' AS "DifferenceInSeconds";

Current Date and Time

With these variables we can get information about current date, time and time zone (TZ).

SELECT CURRENT_DATE as "Date"
     , CURRENT_TIME as "Time(6)+TZ"
     , CURRENT_TIMESTAMP as "Timestamp(6)+TZ"
     , NOW as "Timestamp(6)+TZ"
     , LOCALTIME as "Time(6)"
     , LOCALTIMESTAMP as "Timestamp(6)"
     , CURRENT_TIMEZONE as "Interval second";

Transform Strings to Temporal Data Types

There are several ways how we can create temporal data types from the strings. If we have dates, times, or timestamps in ISO format then we can use casting operator, casting function or convert function.

SELECT DATE '1987-09-23' AS "Date"      , TIME '11:40' AS "Time"      , TIMESTAMP '1987-09-23 11:40' AS "Timestamp"; —————————————————– You can also use TIMETZ, TIMESTAMPTZ.	SELECT CAST('1987-09-23' as date) AS "Date"      , CAST('11:40' as time) AS "Time"      , CAST('1987-09-23 11:40' as timestamp) AS "Timestamp";
SELECT CONVERT('1987-09-23', DATE) AS "Date"      , CONVERT('11:40', TIME) AS "Time"      , CONVERT('1987-09-23 11:40', TIMESTAMP) AS "Timestamp";

If string is not in temporal ISO format, then we have  to use functions str_to_date, str_to_time or str_to_timestamp. These functions have second argument where we describe how to interpret string.

SELECT STR_TO_DATE('23-09-1987', '%d-%m-%Y') AS "Date"
     , STR_TO_TIME('11:40', '%H:%M') AS "Time"
     , STR_TO_TIMESTAMP('23-09-1987 11:40', '%d-%m-%Y %H:%M') AS "Timestamp";

All the specifiers used to describe dates and times can be found on the bottom of this page:
https://www.monetdb.org/documentation-Dec2023/user-guide/sql-functions/date-time-functions/

Here are just the major specifiers:

%S	Seconds (00-59).	%u	Day of week (1-7).	%R	Time "15:36".
%M	Minutes (00-59).	%d	Day of month (01-31).	%T	Time "15:36:40".
%H	Hours (00-23).	%j	Day of year (001-366).
%m	Months (01-12).			%F	ISO 8601 format "2023-05-13".
%Y	Years "1981".	%V	Week of year (01-53).	%s	Seconds from 01.01.1970.
%z	"±hh:mm" timezone.	%Z	Name of timezone.

Do it Reverse, Transform Dates and Times to Strings

Counterpart to functions above, are functions that transform temporal types to strings. Here we have functions DATE_TO_STR, TIME_TO_STR and TIMESTAMP_TO_STR. Again, we are using specifiers to describe how we want our string to look like.

SELECT DATE_TO_STR( DATE '1987-09-23', '%Y-%m-%d') AS "Date string"
     , TIME_TO_STR( TIME '11:40', '%H:%M') AS "Time string"
     , TIMESTAMP_TO_STR( TIMESTAMP '1987-09-23 11:40', '%Y-%m-%d %H:%M') AS "Timestamp string";

Extracting Parts of Times and Dates

It is possible to extract parts of temporal data types by using function EXTRACT.

EXTRACT(YEAR FROM CURRENT_DATE)

We can extract these parts:

YEAR

QUARTER

MONTH

WEEK

DAY

HOUR

MINUTE

SECOND

DOY

DOW

CENTURY

DECADE

It is also possible to use specialized functions to extract temporal parts. Some of these functions have double quotes around them.

"second"(tm_or_ts)	"second"(timetz '15:35:02.002345+01:00')	2,002345
"minute"(tm_or_ts)	"minute"(timetz '15:35:02.002345+01:00')	35
"hour"(tm_or_ts)	"hour"(timetz '15:35:02.002345+01:00')	16
week(dt_or_ts)	week(date '2020-03-22')	12 >ISO week<
"month"(dt_or_ts)	"month"(date '2020-07-22')	7
quarter(dt_or_ts)	quarter(date '2020-07-22')	3
"year"(dt_or_ts)	"year"(date '2020-03-22')	quarter(date '2020-07-22')

dayofmonth(dt_or_ts)	dayofmonth(date '2020-03-22')	22
dayofweek(dt_or_ts)	dayofweek(date '2020-03-22')	7 >Sunday<
dayofyear(dt_or_ts)	dayofyear(date '2020-03-22')	82

century(dt)	century(date '2020-03-22')	21
decade(dt_or_ts)	decade(date '2027-03-22')	202

This two functions can help to extract days or seconds from seconds interval.

"day"(sec_interval)	"day"(interval '3.23' second * (24 * 60 * 60))	3
"second"(sec_interval)	"second"(interval '24' second)	24

Max and Min Date or Time

This is how we can find latest or earliest date or time.

greatest(x, y)	greatest(date '2020-03-22', date '2020-03-25')	date '2020-03-25'
least(x, y)	least(time '15:15:15', time '16:16:16')	time '15:15:15'

Difference Between Two Timestamps – timestampdiff functions

When subtracting two timestamps we can decide in which measure units to get the result.

timestampdiff(ts_tstz, ts_tstz)	select timestampdiff(timestamp '2021-12-31 18:40:40', timestamp '2021-12-30 16:30:20')	interval '94220' second
timestampdiff_min(ts_dt_tz, ts_dt_tz)	select timestampdiff_min(timestamp '2021-12-31 18:40:40', timestamp '2021-12-31 16:30:20')	730
timestampdiff_sec(ts_dt_tz, ts_dt_tz)	select timestampdiff_sec(timestamp '2021-12-31 18:40:40', timestamp '2021-12-31 16:30:20')	7820
timestampdiff_hour(ts_dt_tz, ts_dt_tz)	select timestampdiff_hour(timestamp '2021-12-31 18:40:40', timestamp '2021-12-20 16:30:20')	266
timestampdiff_day(ts_dt_tz, ts_dt_tz)	select timestampdiff_day(timestamp '2021-12-31 18:40:40', timestamp '2021-12-20 16:30:20')	11
timestampdiff_week(ts_tm_tz, ts_tm_tz)	select timestampdiff_week(timestamp '2021-12-31 18:40:40', timestamp '2021-02-20 16:30:20')	44
timestampdiff_month(ts_tm_tz, ts_tm_tz)	select timestampdiff_month(timestamp '2021-12-31 18:40:40', timestamp '2021-02-20 16:30:20')	10
timestampdiff_quarter(ts_tm_tz, ts_tm_tz)	select timestampdiff_quarter(timestamp '2021-12-31 18:40:40', timestamp '2021-02-20 16:30:20')	3
timestampdiff_year(ts_tm_tz, ts_tm_tz)	select timestampdiff_year(timestamp '2021-12-31 18:40:40', timestamp '2024-02-20 16:30:20')	-3

Truncating Timestamp

This is great function that allows to round our Timestamp to 'millennium', 'century', 'decade', 'year', 'quarter', 'month', 'week', 'day', 'hour', 'minute', 'second', 'milliseconds' or 'microseconds'. In example bellow we rounded our timestamp to months. That is why all the values to the right side of the month are getting their minimal values.

sys.date_trunc(field_str, timestamp)

sys.date_trunc('month', timestamp '2020-03-22 13:16:57.734639');

timestamp '2020-03-01 00:00:00.000000'

Unix Time

If we want to transform temporal data in and from Unix time, then we can use these two functions. One will transform number of seconds (counted from 01/01/1970, which is beginning of time) to regular timestamp. Second one will do the reverse. It will transform regular timestamp to seconds from the beginning of Unix time.

sys.epoch(decimal(18,3) seconds)	sys.epoch(1234567890.456)	timestamptz '2009-02-14 01:31:30.456000+02:00'
epoch_ms(dt_or_tm_or_ts_or_interval)	epoch_ms(timestamp '2009-02-13 23:31:30.0')	1234567890

Installation of Pymonetdb Module

Python comes preinstalled on most of the linux distributions. We can check version of our python with a command:

python3 --version           

Now that we know that python is installed, we can install python module which we will use to connect to MonetDB from python. First, we will update the list of available software packages and we will check whether pymonetdb module is available:

sudo apt update
apt search pymonetdb

We will notice that we have two versions of pymonetdb module. Former is for python2 and latter is for python3.

Because Ubuntu's repository has appropriate pymonetdb module, we can install it. For installation we need pip. Pip is a console program used for installing python modules. So, first we need to install pip:

sudo apt install python3-pip                      

After installing pip, we will use it to install pymonetdb module, pip will know which module to install (python3):

pip install pymonetdb  

Pymonetdb module is installed.

Installing of Spyder IDE on Ubuntu

Now we can try to connect to MonetDB from python. For that, I will type python commands into Spyder IDE. We have to first install Spyder IDE on Ubuntu.

sudo apt install spyder                  

We can then start Spyder from the graphical interface (1). This is how spyder looks like (2):

Spyder is a free and open source scientific environment for Python.

Python Script to Connect to MonetDB

Inside of Spyder IDE, I will add this script. This script will first create connection object. Using that connection object, we will create cursor object. Then we can use cursor object to execute our query.

import pymonetdb
connection = pymonetdb.connect(username="voc", password="voc", hostname="localhost", database="voc")
cursor = connection.cursor()
cursor.execute('SELECT * FROM voc.total')
[print( row ) for row in cursor.fetchall() ]

Result of our query will be list of tuples (like [(a,b,c),(1,2,3)] ), where each tuple is one row of a table. We will use list comprehension to print those rows one by one. At the end, Spyder console (1) will show us result.

Pymonetdb Help

If you want to learn more about pymonetdb, you can go to official documentation on this address:

https://pymonetdb.readthedocs.io/en/latest/index.html

Download of Schema

First, we will download sample database from this location:
https://dev.monetdb.org/Assets/VOC/voc_dump.zip

This will give us ZIP file. Inside of it, there is SQL script with the schema for our new database.

What is a Schema?

Database is made of tables, views, indices and so on. Inside of Database, each of these objects belong to some schema. Database is organizationally divided into schemas. There is no overlap between schemas. There could be some special objects that are outside of a scheme, like roles.

Creation, usage and modification of schema elements is strictly done by the user or the role that owns that scheme. During creation of a scheme, we should decide who will be owner, because later it will not be possible to change ownership. If we want several people to own one schema then we should set a role as an owner of a scheme.

Only 'monetdb' user and 'sysadmin' role are allowed to create new schemes.

Creation of a New User

For our schema we will create a new user. First, we will enter mclient with 'monetdb' privileges.

> mclient -u monetdb -d voc           

For creation of a user, we need username (USER), password (WITH PASSWORD), user's full name (NAME), and default schema for that user (SCHEMA). If a user wants to use table from some schema he can use schema as a prefix "myschema.mytable". If a user doesn't provide schema as a prefix, then the default schema will be used.

sql> CREATE USER "voc" WITH PASSWORD 'voc' NAME 'VOC Explorer' SCHEMA "sys";

As a 'monetdb' user we can create new schema. We will say that previously created "voc" user is the owner of that schema.

sql> CREATE SCHEMA "voc" AUTHORIZATION "voc";   

We will set that new schema as the default schema for our user.

sql> ALTER USER "voc" SET SCHEMA "voc";  

sql> \q   -- we can exit mclient with "quit" or "\q"

Populating our Schema with Database Objects

Our schema is currently empty, but we have definitions of all the tables, view, indices … inside of our downloaded SQL script. We will use that script to populate our schema. We type:

> mclient -u voc -d voc ~/Desktop/voc_dump.sql   

We can use mclient command "\d" to list all the tables and views inside of our database. This is what we would get:

sql> \d TABLE voc.craftsmen TABLE voc.impotenten TABLE voc.invoices TABLE voc.passengers TABLE voc.seafarers TABLE voc.soldiers TABLE voc.total TABLE voc.voyages

DBeaver Database Manager Program

We will install DBeaver database manager program in order to peruse our database.

> sudo snap install dbeaver-ce      

This program is GUI program, so we will open it in our desktop environment. In the program we will click on the arrow (1), and then we will select Other (2) to open other servers. There we will find MonetDB. We will click on it (3), and a new dialog will open. In this dialog, host and port are already entered. We just need to enter Database/Schema (4), Username and Password (which is "voc") (5).

DBeaver will not have driver for MonetDB database installed, so it will offer you to download it. Just accept that offer.

At the end, objects of our database will appear inside of pane on the left side of a program. There, we should double click on schema name (1). After that we can select tab "ER Diagram" (2). There, after some rearangement, we will see ER diagram of our database (3). As we can see, tables are organised in star schema with "voyages" table as the only fact table. All tables are connected with foreign key constraints, where foreign key is also primary key inside of dimension tables. The only exception is Inovice table where foreign key columns are not primary key columns, and that is why that relationship is shown with dashed line (4).

DBeaver is an excellent program. If you want to learn more about it you can try this linkedin tutorial.
https://www.linkedin.com/learning/dbeaver-essential-training?trk=learning-topics_trending-courses_related-content-card&upsellOrderOrigin=default_guest_learning