DataPatterns is an ECL bundle that provides some basic data profiling and research tools to an ECL programmer.
This code is installed as an ECL Bundle. Complete instructions for managing ECL Bundles can be found in The ECL IDE and HPCC Client Tools documentation.
Use the ecl command line tool to install this bundle:
ecl bundle install https://github.com/hpcc-systems/DataPatterns.git
You may have to either navigate to the client tools bin directory before executing the command, or use the full path to the ecl tool.
After installation, all of the code here becomes available after you import it:
IMPORT DataPatterns;
Note that is possible to use this code without installing it as a bundle. To do so, simply make it available within your IDE and just ignore the Bundle.ecl file. With the Windows IDE, the DataPatterns directory must not be a top-level item in your repository list; it needs to be installed one level below the top level, such as within your "My Files" folder.
Documentation as pulled from the beginning of Profile.ecl:
Profile() is a function macro for profiling all or part of a dataset.
The output is a dataset containing the following information for each
profiled attribute:
attribute The name of the attribute
given_attribute_type The ECL type of the attribute as it was defined
in the input dataset
best_attribute_type And ECL data type that both allows all values
in the input dataset and consumes the least
amount of memory
rec_count The number of records analyzed in the dataset;
this may be fewer than the total number of
records, if the optional sampleSize argument
was provided with a value less than 100
fill_count The number of rec_count records containing
non-nil values
fill_rate The percentage of rec_count records containing
non-nil values; a 'nil value' is an empty
string or a numeric zero; note that BOOLEAN
attributes are always counted as filled,
regardless of their value; also, fixed-length
DATA attributes (e.g. DATA10) are also counted
as filled, given their typical function of
holding data blobs
cardinality The number of unique, non-nil values within
the attribute
modes The most common values in the attribute, after
coercing all values to STRING, along with the
number of records in which the values were
found; if no value is repeated more than once
then no mode will be shown; up to five (5)
modes will be shown; note that string values
longer than the maxPatternLen argument will
be truncated
min_length The shortest length of a value when expressed
as a string; null values are ignored
max_length The longest length of a value when expressed
as a string
ave_length The average length of a value when expressed
as a string
popular_patterns The most common patterns of values; see below
rare_patterns The least common patterns of values; see below
is_numeric Boolean indicating if the original attribute
was numeric and therefore whether or not
the numeric_xxxx output fields will be
populated with actual values; if this value
is FALSE then all numeric_xxxx output values
should be ignored
numeric_min The smallest non-nil value found within the
attribute as a DECIMAL; the attribute must be
a numeric ECL datatype; non-numeric attributes
will return zero
numeric_max The largest non-nil value found within the
attribute as a DECIMAL; the attribute must be
a numeric ECL datatype; non-numeric attributes
will return zero
numeric_mean The mean (average) non-nil value found within
the attribute as a DECIMAL; the attribute must
be a numeric ECL datatype; non-numeric
attributes will return zero
numeric_std_dev The standard deviation of the non-nil values
in the attribute as a DECIMAL; the attribute
must be a numeric ECL datatype; non-numeric
attributes will return zero
numeric_lower_quartile The value separating the first (bottom) and
second quarters of non-nil values within
the attribute as a DECIMAL; the attribute must
be a numeric ECL datatype; non-numeric
attributes will return zero
numeric_median The median non-nil value within the attribute
as a DECIMAL; the attribute must be a numeric
ECL datatype; non-numeric attributes will return
zero
numeric_upper_quartile The value separating the third and fourth
(top) quarters of non-nil values within
the attribute as a DECIMAL; the attribute must
be a numeric ECL datatype; non-numeric
attributes will return zero
numeric_correlations A child dataset containing correlation values
comparing the current numeric attribute with all
other numeric attributes, listed in descending
correlation value order; the attribute must be
a numeric ECL datatype; non-numeric attributes
will return an empty child dataset; note that
this can be a time-consuming operation,
depending on the number of numeric attributes
in your dataset and the number of rows (if you
have N numeric attributes, then
N * (N - 1) / 2 calculations are performed,
each scanning all data rows)
Most profile outputs can be disabled. See the 'features' argument, below.
Data patterns can give you an idea of what your data looks like when it is
expressed as a (human-readable) string. The function converts each
character of the string into a fixed character palette to producing a "data
pattern" and then counts the number of unique patterns for that attribute.
The most- and least-popular patterns from the data will be shown in the
output, along with the number of times that pattern appears and an example
(randomly chosen from the actual data). The character palette used is:
A Any uppercase letter
a Any lowercase letter
9 Any numeric digit
B A boolean value (true or false)
All other characters are left as-is in the pattern.
Only the top level attributes within a dataset are processed; embedded
records and child recordsets are skipped. SET data types (such as
SET OF INTEGER) are also skipped. If the dataset contains only
embedded records and/or child recordsets, or if fieldListStr is specified
but with attributes that don't actually exist in the top level (or are
invalid) then an error will be produced during compilation time.
This function works best when the incoming dataset contains attributes that
have precise data types (e.g. UNSIGNED4 data types instead of numbers
stored in a STRING data type).
Function parameters:
@param inFile The dataset to process; REQUIRED
@param fieldListStr A string containing a comma-delimited list of
attribute names to process; use an empty string to
process all attributes in inFile; attributes named
here that are not found in the top level of inFile
will be ignored; OPTIONAL, defaults to an
empty string
@param maxPatterns The maximum number of patterns (both popular and
rare) to return for each attribute; OPTIONAL,
defaults to 100
@param maxPatternLen The maximum length of a pattern; longer patterns
are truncated in the output; this value is also
used to set the maximum length of the data to
consider when finding cardinality and mode values;
must be 33 or larger; OPTIONAL, defaults to 100
@param features A comma-delimited string listing the profiling
elements to be included in the output; OPTIONAL,
defaults to a comma-delimited string containing all
of the available keywords:
KEYWORD AFFECTED OUTPUT
fill_rate fill_rate
fill_count
cardinality cardinality
best_ecl_types best_attribute_type
modes modes
lengths min_length
max_length
ave_length
patterns popular_patterns
rare_patterns
min_max numeric_min
numeric_max
mean numeric_mean
std_dev numeric_std_dev
quartiles numeric_lower_quartile
numeric_median
numeric_upper_quartile
correlations numeric_correlations
To omit the output associated with a single keyword,
set this argument to a comma-delimited string
containing all other keywords; note that the
is_numeric output will appear only if min_max,
mean, std_dev, quartiles, or correlations features
are active
@param sampleSize A positive integer representing a percentage of
inFile to examine, which is useful when analyzing a
very large dataset and only an estimated data
profile is sufficient; valid range for this
argument is 1-100; values outside of this range
will be clamped; OPTIONAL, defaults to 100 (which
indicates that the entire dataset will be analyzed)
Here is a very simple example of executing the full data profiling code:
IMPORT DataPatterns;
DataRec := { string source, unsigned bid, string name };
ds := DATASET('~thor::my_sample_data', DataRec, FLAT);
profileResults := DataPatterns.Profile(ds);
OUTPUT(profileResults, ALL, NAMED('profileResults'));
The data profiling code can be easily tested with the included Tests module. Execute the following in either hThor or Roxie (because all of the test data is inline it is best to not use Thor for running these tests):
IMPORT DataPatterns;
EVALUATE(DataPatterns.Tests);
If the tests pass then the execution will succeed and there will be no output. You will see a runtime error if any of the tests fail or if you execute the tests on Thor. Note that it may take some time to compile the code before execution.
This is a function macro that, given a dataset, returns a recordset containing the "best" record definition for the given dataset. By default, the entire dataset will be examined. You can override this behavior by providing a percentage of the dataset to examine (1-100) as the second argument. This is useful if you are checking a very large file and are confident that a sample will provide correct results.
There is an important limitation in this function: Child datasets and embedded record definitions are ignored entirely (this comes from the fact that this function calls the Profile() function, and that function ignores child datasets and embedded records). All other top-level attributes are returned, though, so you can edit the returned string and insert anything that is missing.
Sample call:
IMPORT DataPatterns;
DataRec := { string source, unsigned bid, string name };
ds := DATASET('~thor::my_sample_data', DataRec, FLAT);
recordDefinition := DataPatterns.BestRecordStructure(ds);
OUTPUT(recordDefinition, NAMED('recordDefinition'), ALL);
The result will be a recordset containing only a STRING field. The first record will always contain 'RECORD' and the last record will always contain 'END;'. The records in between will contain declarations for the attributes found within the given dataset. The entire result can be copied and pasted into an ECL code module.
Note that, when outputing the result of BestRecordStructure to a workunit,
it is a good idea to add an ALL flag to the OUTPUT function. This ensures that
all attributes will be displayed. Otherwise, if you have more than 100
attributes in the given dataset, the result will be truncated.
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