01 April 2021

💎SQL Reloaded: Processing JSON Files with Flat Matrix Structure in SQL Server 2016+

Besides the CSV format, many of the data files made available under the open data initiatives are stored in JSON format, which makes data more difficult to process, even if JSON offers a richer structure that goes beyond the tabular structure of CSV files. Fortunately, starting with SQL Server 2016, JSON became a native format, which makes the processing of JSON files relatively easy, the easiness with which one can process the data depending on how they are structured.

Let’s consider as example a JSON file with the world population per country and year that can be downloaded from DataHub (source). The structure behind resembles a tabular model (see the table on the source website), having a flat structure. Just export the data to a file with the JSON extension (e.g. ‘population-figures-by-country.json’) locally (e.g. ‘D:/Data’). The next step is to understand file’s structure. Some repositories provide good documentation in this respect, though there are also many exceptions. Having a JSON editor like Visual Studio which reveals the structure makes easier the process. 

As in the case of CSV files, is needed to infer the data types. There are two alphanumeric fields (Country & Country Code), while the remaining fields are numeric. The only challenge raised by the data seems to be the difference in format between the years 2002 and 2015 in respect to the other years, as the values of the former contain a decimal after comma. All the numeric values should have been whole values. 

It’s recommended to start small and build the logic iteratively. Therefore, for the first step just look at files content via the OPENROWSET function:

-- looking at the JSON file 
SELECT *
FROM OPENROWSET (BULK 'D:\data\population-figures-by-country.json', SINGLE_CLOB)  as jsonfile 

In a second step one can add the OPENJSON function by looking only at the first record: 

-- querying a json file (one record)
SELECT *
FROM OPENROWSET (BULK 'D:\data\population-figures-by-country.json', SINGLE_CLOB)  as jsonfile 
     CROSS APPLY OPENJSON(BulkColumn,'$[0]')

In a third step one can add a few columns (e.g. Country & Country Code) to make sure that the select statement works correctly. 

-- querying a json file (all records, a few fields)
SELECT Country 
, CountryCode 
FROM OPENROWSET (BULK 'D:\data\population-figures-by-country.json', SINGLE_CLOB)  as jsonfile 
     CROSS APPLY OPENJSON(BulkColumn,'$')
 WITH ( 
  Country nvarchar(max) '$.Country'
, CountryCode nvarchar(3) '$.Country_Code'
) AS DAT; 

In a next step can be added all the columns and import the data in a table (e.g. dbo.CountryPopulation) on the fly: 

-- importing a json file (all records) on the fly
SELECT DAT.Country
, DAT.CountryCode
, DAT.Y1960
, DAT.Y1961
, DAT.Y1962
, DAT.Y1963
, DAT.Y1964
, DAT.Y1965
, DAT.Y1966
, DAT.Y1967
, DAT.Y1968
, DAT.Y1969
, DAT.Y1970
, DAT.Y1971
, DAT.Y1972
, DAT.Y1973
, DAT.Y1974
, DAT.Y1975
, DAT.Y1976
, DAT.Y1977
, DAT.Y1978
, DAT.Y1979
, DAT.Y1980
, DAT.Y1981
, DAT.Y1982
, DAT.Y1983
, DAT.Y1984
, DAT.Y1985
, DAT.Y1986
, DAT.Y1987
, DAT.Y1988
, DAT.Y1989
, DAT.Y1990
, DAT.Y1991
, DAT.Y1992
, DAT.Y1993
, DAT.Y1994
, DAT.Y1995
, DAT.Y1996
, DAT.Y1997
, DAT.Y1998
, DAT.Y1999
, DAT.Y2000
, DAT.Y2001
, Cast(DAT.Y2002 as bigint) Y2002
, Cast(DAT.Y2003 as bigint) Y2003
, Cast(DAT.Y2004 as bigint) Y2004
, Cast(DAT.Y2005 as bigint) Y2005
, Cast(DAT.Y2006 as bigint) Y2006
, Cast(DAT.Y2007 as bigint) Y2007
, Cast(DAT.Y2008 as bigint) Y2008
, Cast(DAT.Y2009 as bigint) Y2009
, Cast(DAT.Y2010 as bigint) Y2010
, Cast(DAT.Y2011 as bigint) Y2011
, Cast(DAT.Y2012 as bigint) Y2012
, Cast(DAT.Y2013 as bigint) Y2013
, Cast(DAT.Y2014 as bigint) Y2014
, Cast(DAT.Y2015 as bigint) Y2015
, DAT.Y2016
INTO dbo.CountryPopulation
FROM OPENROWSET (BULK 'D:\data\population-figures-by-country.json', SINGLE_CLOB)  as jsonfile 
     CROSS APPLY OPENJSON(BulkColumn,'$')
 WITH ( 
  Country nvarchar(max) '$.Country'
, CountryCode nvarchar(3) '$.Country_Code'
, Y1960 bigint '$.Year_1960'
, Y1961 bigint '$.Year_1961'
, Y1962 bigint '$.Year_1962'
, Y1963 bigint '$.Year_1963'
, Y1964 bigint '$.Year_1964'
, Y1965 bigint '$.Year_1965'
, Y1966 bigint '$.Year_1966'
, Y1967 bigint '$.Year_1967'
, Y1968 bigint '$.Year_1968'
, Y1969 bigint '$.Year_1969'
, Y1970 bigint '$.Year_1970'
, Y1971 bigint '$.Year_1971'
, Y1972 bigint '$.Year_1972'
, Y1973 bigint '$.Year_1973'
, Y1974 bigint '$.Year_1974'
, Y1975 bigint '$.Year_1975'
, Y1976 bigint '$.Year_1976'
, Y1977 bigint '$.Year_1977'
, Y1978 bigint '$.Year_1978'
, Y1979 bigint '$.Year_1979'
, Y1980 bigint '$.Year_1980'
, Y1981 bigint '$.Year_1981'
, Y1982 bigint '$.Year_1982'
, Y1983 bigint '$.Year_1983'
, Y1984 bigint '$.Year_1984'
, Y1985 bigint '$.Year_1985'
, Y1986 bigint '$.Year_1986'
, Y1987 bigint '$.Year_1987'
, Y1988 bigint '$.Year_1988'
, Y1989 bigint '$.Year_1989'
, Y1990 bigint '$.Year_1990'
, Y1991 bigint '$.Year_1991'
, Y1992 bigint '$.Year_1992'
, Y1993 bigint '$.Year_1993'
, Y1994 bigint '$.Year_1994'
, Y1995 bigint '$.Year_1995'
, Y1996 bigint '$.Year_1996'
, Y1997 bigint '$.Year_1997'
, Y1998 bigint '$.Year_1998'
, Y1999 bigint '$.Year_1999'
, Y2000 bigint '$.Year_2000'
, Y2001 bigint '$.Year_2001'
, Y2002 decimal(19,1) '$.Year_2002'
, Y2003 decimal(19,1) '$.Year_2003'
, Y2004 decimal(19,1) '$.Year_2004'
, Y2005 decimal(19,1) '$.Year_2005'
, Y2006 decimal(19,1) '$.Year_2006'
, Y2007 decimal(19,1) '$.Year_2007'
, Y2008 decimal(19,1) '$.Year_2008'
, Y2009 decimal(19,1) '$.Year_2009'
, Y2010 decimal(19,1) '$.Year_2010'
, Y2011 decimal(19,1) '$.Year_2011'
, Y2012 decimal(19,1) '$.Year_2012'
, Y2013 decimal(19,1) '$.Year_2013'
, Y2014 decimal(19,1) '$.Year_2014'
, Y2015 decimal(19,1) '$.Year_2015'
, Y2016 bigint '$.Year_2016'
) AS DAT; 

As can be seen the decimal values were converted to bigint to preserve the same definition. Moreover, this enables data processing later, as no additional (implicit) conversions are necessary. 

Also, the columns’ names were changed either for simplification/convenience or simply taste. 

Writing such a monster query can be time-consuming, though preparing the metadata into Excel can decrease considerably the effort. With copy-past and a few tricks (e.g. replacing values, splitting columns based on a delimiter) one can easily prepare such a structure:

Source fieldTarget fieldDataTypeValueImport ClauseSelect Clause
CountryCountrynvarchar(max) emen Rep., Country nvarchar(max) '$.Country', DAT.Country
Country_CodeCountryCodenvarchar(3) YEM, CountryCode nvarchar(3) '$.Country_Code', DAT.CountryCode
Year_1960Y1960bigint5172135, Y1960 bigint '$.Year_1960', DAT.Y1960
Year_1961Y1961bigint5260501, Y1961 bigint '$.Year_1961', DAT.Y1961
Year_1962Y1962bigint5351799, Y1962 bigint '$.Year_1962', DAT.Y1962
Year_1963Y1963bigint5446063, Y1963 bigint '$.Year_1963', DAT.Y1963
Year_1964Y1964bigint5543339, Y1964 bigint '$.Year_1964', DAT.Y1964
Year_1965Y1965bigint5643643, Y1965 bigint '$.Year_1965', DAT.Y1965
Year_1966Y1966bigint5748588, Y1966 bigint '$.Year_1966', DAT.Y1966
Year_1967Y1967bigint5858638, Y1967 bigint '$.Year_1967', DAT.Y1967
Year_1968Y1968bigint5971407, Y1968 bigint '$.Year_1968', DAT.Y1968
Year_1969Y1969bigint6083619, Y1969 bigint '$.Year_1969', DAT.Y1969
Year_1970Y1970bigint6193810, Y1970 bigint '$.Year_1970', DAT.Y1970
Year_1971Y1971bigint6300554, Y1971 bigint '$.Year_1971', DAT.Y1971
Year_1972Y1972bigint6407295, Y1972 bigint '$.Year_1972', DAT.Y1972
Year_1973Y1973bigint6523452, Y1973 bigint '$.Year_1973', DAT.Y1973
Year_1974Y1974bigint6661566, Y1974 bigint '$.Year_1974', DAT.Y1974
Year_1975Y1975bigint6830692, Y1975 bigint '$.Year_1975', DAT.Y1975
Year_1976Y1976bigint7034868, Y1976 bigint '$.Year_1976', DAT.Y1976
Year_1977Y1977bigint7271872, Y1977 bigint '$.Year_1977', DAT.Y1977
Year_1978Y1978bigint7536764, Y1978 bigint '$.Year_1978', DAT.Y1978
Year_1979Y1979bigint7821552, Y1979 bigint '$.Year_1979', DAT.Y1979
Year_1980Y1980bigint8120497, Y1980 bigint '$.Year_1980', DAT.Y1980
Year_1981Y1981bigint8434017, Y1981 bigint '$.Year_1981', DAT.Y1981
Year_1982Y1982bigint8764621, Y1982 bigint '$.Year_1982', DAT.Y1982
Year_1983Y1983bigint9111097, Y1983 bigint '$.Year_1983', DAT.Y1983
Year_1984Y1984bigint9472170, Y1984 bigint '$.Year_1984', DAT.Y1984
Year_1985Y1985bigint9847899, Y1985 bigint '$.Year_1985', DAT.Y1985
Year_1986Y1986bigint10232733, Y1986 bigint '$.Year_1986', DAT.Y1986
Year_1987Y1987bigint10628585, Y1987 bigint '$.Year_1987', DAT.Y1987
Year_1988Y1988bigint11051504, Y1988 bigint '$.Year_1988', DAT.Y1988
Year_1989Y1989bigint11523267, Y1989 bigint '$.Year_1989', DAT.Y1989
Year_1990Y1990bigint12057039, Y1990 bigint '$.Year_1990', DAT.Y1990
Year_1991Y1991bigint12661614, Y1991 bigint '$.Year_1991', DAT.Y1991
Year_1992Y1992bigint13325583, Y1992 bigint '$.Year_1992', DAT.Y1992
Year_1993Y1993bigint14017239, Y1993 bigint '$.Year_1993', DAT.Y1993
Year_1994Y1994bigint14692686, Y1994 bigint '$.Year_1994', DAT.Y1994
Year_1995Y1995bigint15320653, Y1995 bigint '$.Year_1995', DAT.Y1995
Year_1996Y1996bigint15889449, Y1996 bigint '$.Year_1996', DAT.Y1996
Year_1997Y1997bigint16408954, Y1997 bigint '$.Year_1997', DAT.Y1997
Year_1998Y1998bigint16896210, Y1998 bigint '$.Year_1998', DAT.Y1998
Year_1999Y1999bigint17378098, Y1999 bigint '$.Year_1999', DAT.Y1999
Year_2000Y2000bigint17874725, Y2000 bigint '$.Year_2000', DAT.Y2000
Year_2001Y2001bigint18390135, Y2001 bigint '$.Year_2001', DAT.Y2001
Year_2002Y2002decimal(19,1) 18919179.0, Y2002 decimal(19,1) '$.Year_2002', Cast(DAT.Y2002 as bigint) Y2002
Year_2003Y2003decimal(19,1) 19462086.0, Y2003 decimal(19,1) '$.Year_2003', Cast(DAT.Y2003 as bigint) Y2003
Year_2004Y2004decimal(19,1) 20017068.0, Y2004 decimal(19,1) '$.Year_2004', Cast(DAT.Y2004 as bigint) Y2004
Year_2005Y2005decimal(19,1) 20582927.0, Y2005 decimal(19,1) '$.Year_2005', Cast(DAT.Y2005 as bigint) Y2005
Year_2006Y2006decimal(19,1) 21160534.0, Y2006 decimal(19,1) '$.Year_2006', Cast(DAT.Y2006 as bigint) Y2006
Year_2007Y2007decimal(19,1) 21751605.0, Y2007 decimal(19,1) '$.Year_2007', Cast(DAT.Y2007 as bigint) Y2007
Year_2008Y2008decimal(19,1) 22356391.0, Y2008 decimal(19,1) '$.Year_2008', Cast(DAT.Y2008 as bigint) Y2008
Year_2009Y2009decimal(19,1) 22974929.0, Y2009 decimal(19,1) '$.Year_2009', Cast(DAT.Y2009 as bigint) Y2009
Year_2010Y2010decimal(19,1) 23606779.0, Y2010 decimal(19,1) '$.Year_2010', Cast(DAT.Y2010 as bigint) Y2010
Year_2011Y2011decimal(19,1) 24252206.0, Y2011 decimal(19,1) '$.Year_2011', Cast(DAT.Y2011 as bigint) Y2011
Year_2012Y2012decimal(19,1) 24909969.0, Y2012 decimal(19,1) '$.Year_2012', Cast(DAT.Y2012 as bigint) Y2012
Year_2013Y2013decimal(19,1) 25576322.0, Y2013 decimal(19,1) '$.Year_2013', Cast(DAT.Y2013 as bigint) Y2013
Year_2014Y2014decimal(19,1) 26246327.0, Y2014 decimal(19,1) '$.Year_2014', Cast(DAT.Y2014 as bigint) Y2014
Year_2015Y2015decimal(19,1) 26916207.0, Y2015 decimal(19,1) '$.Year_2015', Cast(DAT.Y2015 as bigint) Y2015
Year_2016Y2016bigint27584213, Y2016 bigint '$.Year_2016', DAT.Y2016

Based on this structure, one can add two further formulas in Excel to prepare the statements as above and then copy the fields (last two columns were generated using the below formulas): 

=", " & TRIM(B2) & " " & C2 & " '$." & TRIM(A2) & "'" 
=", DAT." & TRIM(B2)

Consuming data in which the values are stored in a matrix structure can involve further challenges sometimes, even if this type of storage tends to save space. For example, adding the values for a new year would involve extending the table with one more column, while performing calculations between years would involve referencing each column in formulas. Therefore, transforming the data from a matrix to a normalized structure can have some benefit. This can be achieved by writing a query via the UNPIVOT operator:

-- unpivoting the data 
SELECT RES.Country
, RES.CountryCode
, Cast(Replace(RES.[Year], 'Y', '') as int) [Year]
, RES.Population
--INTO dbo.CountryPopulationPerYear
FROM 
( -- basis data
	SELECT Country
	, CountryCode
	, Y1960, Y1961, Y1962, Y1963, Y1964, Y1965, Y1966, Y1967, Y1968, Y1969
	, Y1970, Y1971, Y1972, Y1973, Y1974, Y1975, Y1976, Y1977, Y1978, Y1979
	, Y1980, Y1981, Y1982, Y1983, Y1984, Y1985, Y1986, Y1987, Y1988, Y1989
	, Y1990, Y1991, Y1992, Y1993, Y1994, Y1995, Y1996, Y1997, Y1998, Y1999
	, Y2000, Y2001, Y2002, Y2003, Y2004, Y2005, Y2006, Y2007, Y2008, Y2009
	, Y2010, Y2011, Y2012, Y2013, Y2014, Y2015, Y2016
	FROM dbo.CountryPopulation
) DAT
UNPIVOT  -- unpivot logic
   (Population FOR [Year] IN  (Y1960, Y1961, Y1962, Y1963, Y1964, Y1965, Y1966, Y1967, Y1968, Y1969
, Y1970, Y1971, Y1972, Y1973, Y1974, Y1975, Y1976, Y1977, Y1978, Y1979
, Y1980, Y1981, Y1982, Y1983, Y1984, Y1985, Y1986, Y1987, Y1988, Y1989
, Y1990, Y1991, Y1992, Y1993, Y1994, Y1995, Y1996, Y1997, Y1998, Y1999
, Y2000, Y2001, Y2002, Y2003, Y2004, Y2005, Y2006, Y2007, Y2008, Y2009
, Y2010, Y2011, Y2012, Y2013, Y2014, Y2015, Y2016)
) RES

Also this can be performed in two steps, first preparing the query, and in a final step inserting the data into a table (e.g. dbo.CountryPopulationPerYear) on the fly (re-execute the previous query after uncommenting the INSERT clause to generate the table). 

--reviewing the data 
SELECT Country
, CountryCode
, AVG(Population) AveragePopulation
, Max(Population) - Min(Population) RangePopulation
FROM dbo.CountryPopulationPerYear
WHERE [Year] BETWEEN 2010 AND 2019
GROUP BY Country
, CountryCode
ORDER BY Country

On the other side making comparisons between consecutive years is easier when using a matrix structure: 

--reviewing the data 
SELECT Country
, CountryCode
, Y2016
, Y2010
, Y2010-Y2010 [2016-2010]
, Y2011-Y2010 [2011-2010]
, Y2012-Y2011 [2011-2011]
, Y2013-Y2012 [2011-2012]
, Y2014-Y2013 [2011-2013]
, Y2015-Y2014 [2011-2014]
, Y2016-Y2015 [2011-2015]
FROM dbo.CountryPopulation
ORDER BY Country

Unless the storage space is a problem, in theory one can store the data in both formats as there can be requests which can benefit from one structure or the other. 

Happy coding!

No comments:

Related Posts Plugin for WordPress, Blogger...

About Me

My photo
Koeln, NRW, Germany
IT Professional with more than 24 years experience in IT in the area of full life-cycle of Web/Desktop/Database Applications Development, Software Engineering, Consultancy, Data Management, Data Quality, Data Migrations, Reporting, ERP implementations & support, Team/Project/IT Management, etc.