The Amount of Data That Can Be Transferred From One Point to Another in One Second Is Called ____.

PostgreSQL

An introduction to PostgreSQL information types

Introduction

Ane of the principal features of relational databases in general is the ability to define schemas or table structures that exactly specify the format of the data they will contain. This is washed by prescribing the columns that these structures contain forth with their data type and any constraints.

Data types specify a full general pattern for the data they accept and store. Values must adhere to the requirements that they outline in order to be accepted by PostgreSQL. While it is possible to define custom requirements, data types provide the basic building blocks that allow PostgreSQL to validate input and work with the information using advisable operations.

PostgreSQL includes a wide range of data types that are used to label and validate that values arrange to advisable types. In this guide, nosotros will discuss the most common information types available in PostgreSQL, the different input and output formats they use, and how to configure various fields to meet your applications' needs.

What are the information types in PostgreSQL?

Earlier going into item, let's have a wide view of what data types PostgreSQL provides.

PostgreSQL supports a wide range of information types suitable for various types of uncomplicated and complex information. These include:

• integer
• smallint
• bigint
• serial
• smallserial
• bigserial
• numeric
• float
• double precision
• money
• char
• varchar
• text
• boolean
• date
• time
• timestamp
• timestamptz
• interval
• enum
• uuid
• json
• jsonb
• xml
• inet (network address)
• cidr (network address)
• macaddr
• polygon
• line
• lseg (line segment)
• box (rectangular box)
• bytea (hex format)
• tsvector (text search)
• tsquery (text search)

We'll cover the most common of these in more depth throughout this guide.

Getting started with PostgreSQL data types

PostgreSQL comes with a large number of types built-in to the software itself. It too allows you lot to ascertain your own complex types past combining types of unlike kinds and specifying their parameters. This allows administrators to precisely define the types of data they look each column to accept when using CREATE Tabular array among other commands. PostgreSQL can then automatically check proposed values to ensure they lucifer the provided criteria.

As yous get started with types, it'southward of import to remember that types lone are not ever a consummate solution to data validation, simply a component. Other database tools, similar constraints besides accept a part to play in defining correctness. Withal, data types are often the first line of defence against invalid information.

For many cases, the general types provided by PostgreSQL are advisable for the kinds of data you'll be storing. Nevertheless, sometimes, more specific types are available that can provide additional operators, associated functions, or congenital-in constraint-like validation. For case, while you could store the coordinates of a geometric signal in two different number columns, the provided point blazon is purpose built to shop and validate exactly this type of data. When choosing types, check to see that you are using the most specific type applicable to your use case.

Numbers and numeric values

PostgreSQL includes a skilful range of numeric data types suitable for different scenarios. These include integers, floating points, arbitrary precision, and a special integer type with additional features chosen serial.

Integers

The integer data type is a category of types used to store numbers without any fractions or decimals. These can be either positive or negative values, and different integer types can store unlike ranges of numbers. Integer types with smaller ranges of acceptable values have up space than those with wider ranges.

The bones list of integer types includes the following:

Integer type	Length	Applicable range	Comment
integer	4 bytes	-2147483648 to 2147483647	This is the most common integer type to use equally information technology represents a good trade off between storage and expressiveness for many practical applications.
smallint	2 bytes	-32768 to 32767	It is rare to use this except in places with tight storage constraints.
bigint	viii bytes	-9223372036854775808 to 9223372036854775807	Typically this type is reserved for scenarios where the integer type would accept an insufficient range.

The types above are limited by their valid range. Any value outside of the range volition result in an error.

In addition to the standard integer types mentioned above, PostgreSQL includes a prepare of special integer types called series types. These types are primarily used to create unique identifiers, or primary keys, for records.

By default, serial types will automatically utilize the side by side integer in an internally tracked sequence when new records are added. Then if the last integer used in a serial cavalcade was 8559, by default, the next tape will automatically use 8560. To guarantee that each value is unique, you can farther add a UNIQUE constraint on the column.

There is a serial type for each of the integer sizes mentioned earlier, which dictates the type of integer used in the sequence:

• serial: a series type that automatically increments a column with the next integer value.
• smallserial: a series blazon that automatically increments a cavalcade with the next smallint value.
• bigserial: a serial type that automatically increments a cavalcade with the next bigint value.

Arbitrary precision

Arbitrary precision types are used to control the corporeality of precision or specificity possible for a number with decimals. In PostgreSQL, this can be controlled by manipulating two factors: precision and scale.

Precision is the maximum corporeality of full digits that a number can have. In dissimilarity, scale is the number of digits to the correct of the decimal point. Past manipulating these numbers, you lot can control how big the partial and non-fractional components of a number are allowed to be.

These two arguments are used to control arbitrary precision using the numeric data type. The numeric blazon takes zero to 2 arguments.

With no arguments, the column tin shop values of whatever precision and calibration:

When a single argument is provided, it is interpreted as the precision of the column with calibration set up to 0. Though not stored this way on deejay, this effectively allows you lot to specify the maximum number of digits in an integer-like number (no fractional or decimal components). For example, if you demand a v digit whole number, you can specify:

Specify precision followed by scale when configuring a cavalcade using both controls. PostgreSQL volition round the decimal component of whatever input to the correct number of digits using the scale number. Later on, it will check whether the complete rounded number (both the whole and decimal components) exceeds the given precision number. If information technology does, PostgreSQL will produce an error.

For example, nosotros can specify a column with a total precision of v and a scale of 2:

This column would accept the following behavior:

Input value	Rounded value	Accepted (fits precision)?
400.28080	400.28	Yes
viii.332799	8.33	Yes
11799.799	11799.fourscore	No
11799	11799	Aye
2802.27	2802.27	No

Floating indicate

Floating point numbers are some other mode to express decimal numbers, but without exact, consequent precision. Instead, floating point types only have a concept of a maximum precision which is oftentimes related to the architecture and platform of the hardware.

For example, to limit a floating point column to 8 digits of precision, you can type:

Because of these blueprint choices, floating point numbers can work with numbers with large number of decimals efficiently, but not always exactly. The internal representation of numbers may cause slight differences betwixt the input and output. This can cause unexpected behavior when comparing values, doing floating point math, or performing operations that require exact values.

Double precision (floating bespeak) vs numeric

Both floating point numbers provided by types similar float and double precision and arbitrary precision numbers provided by the numeric type can be used to store decimal values. How practise yous know which one to use?

The full general rule is that if you need exactness in your calculations, the numeric blazon is always the better choice. The numeric type will store values exactly as they are provided, meaning that the results are entirely anticipated when retrieving or calculating over values. The numeric blazon is called arbitrary precision because you specify the amount of precision the blazon requires and it will shop that verbal amount of digits in the field.

In contrast, types like float and double precision are variable precision types. The amount of precision they maintain depends on the input value. When they reach the end of their allowed level of precision, they may round the remaining digits, leading to differences between the submitted and retrieved values.

So when would yous utilise variable precision types? Variable precision types similar float and double precision are well suited for scenarios where verbal values are not necessary (for case, if they'll be rounded anyways) and when speed is highly valuable. Variable precision will generally offering performance benefits over the numeric type.

Monetary values

PostgreSQL includes a special coin type that is used to store numeric values representing monetary units.

The coin type does not take arguments, so the column definitions use simply the type:

The money type has a fixed partial component that takes its precision from the lc_monetary PostgreSQL localization option. If that variable is undefined, the precision is taken from the LC_MONETARY environment variable in Linux or Unix-like environments or equivalent locale settings in other operating systems. In many instances, the precision volition be gear up to use 2 decimal places to friction match common usage.

Because of this precision, it is recommended to but apply the coin type when fractions of cents are not possible or important. Similarly, since no currency is attached to the type, information technology is non well suited for situations where currency conversions are necessary. The money blazon has great operation for simple employ cases, however, so in spite of these constraints, it tin can nevertheless be valuable.

Because of the dependency on locale settings of the PostgreSQL installation or execution environment, money values, information technology is critical to ensure that these values lucifer when transferring data between different systems.

Care must also be taken when casting values in and out of the coin type since it tin can lose precision information when converting between certain types. It is safe for money values to cast to and from the numeric blazon (used for arbitrary precision, as shown above), so information technology is recommended to e'er utilise numeric as an intermediary before performing converting to other types.

Text and characters

PostgreSQL's graphic symbol types and string types can be placed into two categories: fixed length and variable length. The option between these two affects how PostgreSQL allocates space for each value and how it validates input.

The simplest character-based data type within PostgreSQL is the char type. With no arguments, the char type accepts a single character as input:

When a positive integer is provided in the announcement, the char column will store a fixed length character string equal to the number of characters specified:

If a string is provided with fewer characters, bare spaces volition be appended to pad the length:

Input	# of input characters	Stored value	# of stored characters
'tree'	4	'tree             '	10

If a string is given with greater than the allowed number of characters, PostgreSQL volition enhance an error. As an exception to this rule, if the flood characters are all spaces, PostgreSQL will just truncate the excess spaces to fit the field. PostgreSQL doesn't recommend using char unless these characteristics are specifically desirable.

The alternative to fixed length character fields are variable length fields. For this, PostgreSQL provides the varchar type. The varchar type stores characters with no fixed size. Past default, with no integer given, varchar columns will accept strings of whatsoever length:

Past defining a varchar with a positive integer, you lot can prepare a maximum cord length:

This differs from using the char type with an integer in that varchar volition not pad the value if the input does not meet the maximum field length:

Input	# of input characters	Stored value	# of stored characters
'tree'	4	'tree'	iv

If the string is greater than the maximum length, PostgreSQL will throw an fault. The same truncation beliefs that'southward nowadays in char fields occurs here: if the flood characters are spaces, they will exist truncated to fit inside the maximum graphic symbol length.

The third information type that PostgreSQL provides for strings and character storage is called text . This type operates exactly similar the varchar blazon without a maximum field length. It is used to shop strings of any length:

There is no difference between these two type declarations, so they can be used interchangeably.

Booleans

PostgreSQL uses the boolean or bool blazon to express true and false values:

In keeping with SQL standards, the PostgreSQL boolean data type can actually express iii states:

• true: Represented by the SQL keyword TRUE. Equally input values, the post-obit strings also evaluate to true: truthful, aye, on, and 1. The output function represents true values with the string "t".
• false: Represented by the SQL keyword False. As input values, the following strings likewise evaluate to false: false, no, off, and 0. The output role represents false values with the string "f".
• unknown: Represented by the SQL keyword NULL. In the context of SQL, a NULL value in a boolean column is meant to point that the value is unknown.

Equally mentioned above, PostgreSQL is somewhat flexible on boolean input values, simply stores values using the defended True, FALSE, and Zippo keywords.

Care must be taken when working with the boolean Nix. While PostgreSQL can correctly interpret Truthful and Faux as booleans, it cannot make that assumption for NULL due to its multiple uses. You can explicitly bandage NULL values to the boolean type in these situations to avoid this ambiguity:

Dates and time

PostgreSQL has robust support for representing dates, times, and temporal intervals.

Dates

The date blazon can store a date without an associated fourth dimension value:

When processing input for date columns, PostgreSQL tin can interpret many unlike formats to determine the correct date to store. Some formats are based on well known standards, while others are vernacular formats used in many real world contexts.

The full range of input formats for dates that PostgreSQL understands is shown in the "Engagement Input" table in the PostgreSQL documentation.

To deal with ambiguous input, similar 07/12/2019 (which could be interpreted as either July 12, 2022 or December 07, 2022 depending on format), y'all can gear up the expected ordering using the DateStyle parameter. This tin be ready to DMY, MDY, or YMD to define the expected ordering. By default, PostgreSQL will fix it to MDY or use the lc_time locale to determine the appropriate ordering.

PostgreSQL can too output dates using various formats:

• ISO: Outputs dates according to ISO 8601. March 18, 2009 would be represented as 2009-03-18.
• SQL: The traditional SQL date format. March 18, 2009 would be represented as 03/18/2009.
• Postgres: Mirrors ISO format for dates. March 18, 2009 would be represented every bit 2009-03-18.
• German: The High german regional style. March xviii, 2009 would be represented as 18.03.2009.

Both the SQL and Postgres formats respect the DateStyle value, mentioned earlier, to determine the ordering of the month, day, and years in output.

Time

The time data blazon (besides called time without fourth dimension zone) can store a specific fourth dimension of day without an associated timezone or date.

PostgreSQL does not recommend using time with fourth dimension zone, the time type's variant that pairs a time zone with the clock time. This is due to complications and ambiguities that arise that cannot be resolved without additional context, like an associated date. For times that require a time zone component, the timezonetz type, covered in the next department, is a good culling that provides the engagement component context.

When processing input for fourth dimension columns, PostgreSQL can translate many different formats to determine the correct fourth dimension to store. Most of these are variations on the ISO 8601 standard, with flexibility to catch different variations.

The full range of input formats for times that PostgreSQL understands is shown in the "Time Input" table in the PostgreSQL documentation.

PostgreSQL can store time values with a microsecond resolution. The amount of precision tin be defined when the column is created by specifying a '(p)' or precision value, which can be whatever integer between 0 and 6.

For example, to store time values with iii decimal places of fractional seconds, yous could ascertain the time column similar this:

If no (p) value is provided, the column volition store according to the input's precision, up to vi decimal places.

When outputting times, PostgreSQL relies on the same format definitions available for date options. These are mostly result in the same or similar outputs:

• ISO: Outputs time according to ISO 8601. 04:28 PM and 52 seconds would be represented as sixteen:28:52.
• SQL: The traditional SQL fourth dimension format. 04:28 PM and 52 seconds would be represented as xvi:28:52.00.
• Postgres: Uses the Unix appointment / fourth dimension format. 04:28 PM and 52 seconds would be represented equally 16:28:52.
• German: The German regional mode. 04:28 PM and 52 seconds would be represented every bit 16:28:52.00.

Equally yous can see, the output format doesn't have much of an affect on time representations as it does on dates. The principal deviation can be seen in the timestamp output that we'll see adjacent.

Timestamps

PostgreSQL can represent timestamps, a combination of a engagement and time used to stand for a specific moment in time, in two different variations: with and without an associated fourth dimension zone. Timestamps with a specified time zone tin can exist stored in the timestamptz data type (also known as timestamp with fourth dimension zone), while the timestamp data type (can as well write as timestamp without time zone) is used for timestamps without a time zone.

Like the fourth dimension type, the timestamp and timestamptz types can take a (p) value to command the amount of precision that is stored. This tin again be a number between zero and six.

To declare a timestamp column with three digits of precision, you could type:

To do the same with a timestamp that includes a timezone, type:

When inputting values for timestamp columns, all that is needed is a valid date format followed by a valid time format, separated by a infinite. PostgreSQL as well recognizes the "Postgres original mode" format, which is similar to the default output used by the Unix engagement command, merely with the time zone, if present, at the end:

              
                
                                      
                                              Wed Mar 18 16:28:52 2009 EST                                          
                                  
              
            

For timestamp columns, any provided time zone values will be ignored.

Providing values for timestamptz fields are exactly the aforementioned as for timestamp but with the addition of a time zone. Time zones tin can be specified in a number of different formats, which use labels, abbreviations, or offsets from UTC. The time zone indicator designation is included after the date and time in timestamps.

When storing timestamptz values, PostgreSQL converts the input to UTC for storage. This simplifies the storage since the time zone used for output may be different from the input.

When outputting timestamps, the same iv formats that influence date and fourth dimension can influence how PostgreSQL represents timestamp values:

• ISO: Outputs timestamps according to ISO 8601. The point in time of 04:28 PM and 52 seconds on March xviii, 2009 in Eastern Standard Fourth dimension would exist represented equally 2009-03-eighteen 16:28:52-05. For timestamp columns, which do not include the fourth dimension zone, the -05 would be omitted. Rather than separating the engagement and time components with a capital 'T', as ISO 8601 defines, PostgreSQL uses a space to delimit these fields.
• SQL: The traditional SQL date format. The point in time of 04:28 PM and 52 seconds on March 18, 2009 in Eastern Standard Time would be represented every bit 03/18/2009 16:28:52.00 EST. For timestamp columns, which do not include the time zone, the EST would be omitted.
• Postgres: Resembles the format used past the Unix date command. The point in time of 04:28 PM and 52 seconds on March xviii, 2009 in Eastern Standard Time would be represented as Wed Mar 18 16:28:52 2009 EST. For timestamp columns, which practise not include the time zone, the EST would be omitted.
• High german: The German regional style. The bespeak in time of 04:28 PM and 52 seconds on March eighteen, 2009 in Eastern Standard Fourth dimension would be represented every bit 18.03.2009 sixteen:28:52.00 EST. For timestamp columns, which do not include the time zone, the EST would exist omitted.

Intervals

PostgreSQL tin can too store and piece of work with values that represent temporal intervals. These basically describe the amount of time between two specific timestamps.

Like time, timestamp and timestamptz, the interval data type tin can represent fourth dimension differences to the microsecond level. Once again, the (p) statement is used to represent the corporeality of precision, or decimal places, the number will tape, which can range from zilch to six:

While the (p) argument affects how fractions of seconds are stored, the interval type has some other statement that tin alter the amount of specificity more than generally. By providing one of the follow values when defining an interval column, you tin can command the level of detail by limiting the fields used to shop interval data:

• Twelvemonth: Shop only the number of years
• Month: Store but the number of months
• DAY: Shop merely the number of days
• Hr: Shop only the number of hours
• MINUTE: Shop just the number of minutes
• SECOND: Store merely the number of seconds
• YEAR TO Month: Store merely the number of years and months
• Twenty-four hours TO Hr: Store just the number of days and hours
• DAY TO Infinitesimal: Store simply the number of days, hours, and minutes
• 24-hour interval TO SECOND: Store only the number of days, hours, minutes, and seconds
• 60 minutes TO MINUTE: Store only the number of hours and minutes
• Hour TO 2d: Store but the number of hours, minutes, and seconds
• Minute TO SECOND: Store only the number of minutes and seconds

When specifying both the fields to shop and precision, the fields come up offset. Therefore, to create a cavalcade with 5 digits of precision that just stores the days, hours, minutes, and seconds of a given interval, you lot could type:

                
                  
                                          
                                                  INTERVAL DAY TO SECOND (v)                                              
                                      
                
              

Be aware that you lot tin can only specify precision if your alleged field ranges include the seconds value, since this is the but case where this argument matters.

There are a number of different ways to format input when calculation values to an interval column. The most straightforward way is to specify the amount and the unit of each cavalcade or field you wish to provide, separated by a infinite. For example:

                
                  
                                          
                                                  vii days 3 hours 27 minutes 8 seconds                                              
                                      
                
              

To indicate that the interval is counting in the reverse direction, add the ago keyword at the end:

                
                  
                                          
                                                  7 days 3 hours 27 minutes 8 seconds ago                                              
                                      
                
              

The interval described above can too be represented without units by providing a day field followed past the clock units separated by colons:

Similarly, intervals that only express years and months can be represented past a year, a nuance, and a month. And so 38 years and 4 months would expect like:

PostgreSQL as well understands abbreviated input based on ISO 8601 timestamps, which can be used to correspond intervals that utilize a greater number of fields. There are two split input formats based on this standard.

The kickoff uses the post-obit unit abbreviations, represented by the bold component of the following fields:

• Years
• Months
• Westeeks
• Days
• Hours
• Chiliadinutes
• Southwardeconds

You may notice that 1000 is used to label both months and minutes. In this format, the date component is separated from the time component by a "T". If the M appears before the T, it is interpreted as months; if it occurs afterward the T, it means minutes.

Formats based on ISO 8601 begin with a P and then comprise the interval string. So to represent an interval of 4 years, 2 months, 7 days, 3 hours, 27 minutes, and 8 seconds, the following string would work:

The other ISO 8601 format does not apply unit abbreviations at all. Instead, it separates the engagement components with dashes and the fourth dimension components with colons. Once again, the string begins with a P and separates the date and time components with a T. The same interval expressed earlier could be written equally:

PostgreSQL can output values from interval columns in several formats. The output style is determined by the intervalstyle setting, which tin can be one of the post-obit:

• postgres: The default fashion. This format uses the units years, mons, and days, each separated past a space to represent date components. It uses HH:MM:SS to correspond time components.
• postgres_verbose: This format begins with an ampersand (@). It includes units or unit abbreviations for all fields, separated by spaces: years, mons, days, hours, mins, and secs.
• sql_standard: Follows the SQL standard output spec. Years and months are separated past a nuance: YYYY-MM. Later, day and time components are represented past an independent day field and a HH:MM:SS fourth dimension field. The complete representation would await like: YYYY-MM D HH:MM:SS
• iso_8601: This mode produces output with ISO 8601's "format with designators" (the commencement ISO 8601 style described above). Replacing the pound signs with actual values, the output would look similar this: P#Y#One thousand#DT#H#Thou#S

Other useful types

Forth with the types we covered with some depth above, there are additional types that are useful in specific scenarios. Nosotros'll cover these briefly to give you an idea of how to employ them and when they may be useful.

Does PostgreSQL support user divers types?

PostgreSQL supports user defined types in a few different means.

Enumerated types

PostgreSQL enumerated types are user-defined types that have a ready number of valid values. This functions similar to a drib downward card in that a choice can be made from a specific set of options. For example, an enum type called flavour could be created with the values winter, spring, summer, and autumn.

To employ an enum blazon as a column, y'all must first ascertain information technology to declare its name and range of values. Y'all can create the season type nosotros described above by typing:

                  
                    
                                              
                                                      CREATE TYPE season AS ENUM ('winter', 'jump', 'summer', 'autumn');                                                  
                                          
                  
                

The syntax specifies the type name, the ENUM category, as well every bit the possible values.

After, you can ascertain a column to be of the season type as you would any other column when creating a table:

Other user defined types

Other types can also be divers with the CREATE Blazon command. These include:

• Blended types: Blended types are types that are defined every bit a combination of two or more different types. For instance, you could create an event type that combines a geographical location and a timestamp to pinpoint a specific time and place.
• Range types: Range types include a valid range for a specified data type. While PostgreSQL includes some range types by default, the CREATE Blazon control allows you to create your ain.
• Base types: Base types are used to define a completely new blazon of data that isn't reliant on modifying existing types. To practice this, you lot'll need to lawmaking upwardly type functions in C to show PostgreSQL how to input, output, and process the data.

UUID

Universally Unique Identifiers, or UUIDs, are 128-chip numbers used to distinctively place pieces of information. They are used in many dissimilar contexts in society to assign a global identifier that is extremely unlikely to be assigned elsewhere. PostgreSQL includes the uuid type to piece of work with these values:

UUIDs have 32 digits, and are generally visually separated into five groups with hyphens (8 digits, 4 digits, iv digits, 4 digits, and finally 12 digits):

                
                  
                                          
                                                  ########-####-####-####-############                                              
                                      
                
              

Each placeholder contains a hexadecimal digit (0 through 9, plus the lower instance messages "a" through "f"). PostgreSQL uses this standard format for output.

For input, PostgreSQL understands a number of formats including using upper instance letters, dissimilar digit groupings, no digit groupings, and surrounding the UUID with curly brackets.

JSON

PostgreSQL supports columns in JSON using the json and jsonb format. Information stored as json is stored as-is, while data stored with jsonb is interpreted and processed and stored in binary for faster execution and processing. PostgreSQL tin as well index jsonb columns for meliorate performance. In general, jsonb is recommended for JSON data for this reason:

At that place are some slight differences between the two formats. The json type preserves incidental white space, key ordering, and duplicate keys. The jsonb format removes insignificant white space, overwrites duplicate keys, and provides no key ordering guarantees.

PostgreSQL includes JSON operators, can alphabetize jsonb columns, test whether jsonb objects incorporate other jsonb objects, and can transform values to data types used in dissimilar languages. These are outside of the scope of this guide, but will be covered in a hereafter article on working with JSON with PostgreSQL.

Conclusion

We've covered many of the most mutual information types used in PostgreSQL in this guide. While these provide a proficient starting point for data types in PostgreSQL, additional types are bachelor to shop other forms of data. Using the most advisable types for your data allows you lot to utilize the database system to validate and operate on your data hands.

Understanding information types is essential when designing schemas and tables in PostgreSQL. It also affects how to collaborate with the database from your applications, as the type system influences how data must exist formatted and how information technology may be expressed when outputted. Learning nearly the options available within PostgreSQL and the side effects your choices might take is the best way to plan ahead when designing your information structures.

If you are using Prisma Client to work with your PostgreSQL databases, you lot tin can find a mapping between some of the common PostgreSQL and Prisma types in Prisma'southward PostgreSQL data connectors docs.

RELATED ON PRISMA.IO

In the data model used by Prisma schema, data types are represented by field types. Bank check out our documentation to learn more.

Prisma is an open up-source database toolkit for Typescript and Node.js that aims to brand app developers more productive and confident when working with databases.

Well-nigh the Author(s)

Justin Ellingwood

Justin has been writing about databases, Linux, infrastructure, and developer tools since 2013. He currently lives in Berlin with his wife and 2 rabbits. He doesn't usually have to write in the third person, which is a relief for all parties involved.

wadeslin1939.blogspot.com

Source: https://www.prisma.io/dataguide/postgresql/introduction-to-data-types

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