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%global _empty_manifest_terminate_build 0
Name: python-ts-ids-es-json-generator
Version: 1.0.1
Release: 1
Summary: Python utility for generating elasticsearch.json from schema.json for an IDS
License: Apache License 2.0
URL: https://github.com/tetrascience/ts-ids-es-json-generator
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d5/1c/c0b68d36010671a96d922040634515e4acdc5da8cb939f1658dea7a1fc6c/ts-ids-es-json-generator-1.0.1.tar.gz
BuildArch: noarch
Requires: python3-jsonref
Requires: python3-pydash
%description
# ts-ids-es-json-generator
## Introduction
This simple utility generates `elasticsearch.json` using `schema.json`.
## How to Use
To run the generator, simply provide the program with the location of the IDS root directory:
```
$ pipenv run python -m ids_es_json_generator ~/path/to/ids/folder
```
`elasticsearch.json` may be manually edited after automatic generation (e.g. to update non-searchable fields).
## Output
The script saves `elasticsearch.json` to the same directory you provided.
## Assumptions
The generator operates under the following assumptions:
- It's the IDS designer's responsibility to make sure the schema and `elasticsearch.json` are correct.
The generator will not validate the IDS for you.
## Restrictions
- JSON pointers are dereferenced to create the mapping, so schemas with circular references or missing references will fail.
## Tests
Run the tests using the following command:
`$ pipenv run python -m pytest`
## How it works
Anywhere an array of objects appears in the schema, a `nested` field type will be created in the output `elasticsearch.json`, including arrays of objects contained anywhere within other arrays of objects.
The steps are:
1. The schema is loaded into a dict with all of its JSON pointers dereferenced using `jsonref`, so that nested fields involving `definitions` are included in the elasticsearch mapping.
2. `find_array_object` will find all arrays of objects and create a dict representing their locations in the schema (see test case `test_array_simple_nested_case` in `__tests__/unit/test_find_array_object.py`).
3. `generate_es_mapping` uses this dict to create the elasticsearch mapping (see test case `test_nested_keys_case` in `__tests__/unit/test_generate_es_mapping.py`).
See the example files and test cases for more example usage.
%package -n python3-ts-ids-es-json-generator
Summary: Python utility for generating elasticsearch.json from schema.json for an IDS
Provides: python-ts-ids-es-json-generator
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-ts-ids-es-json-generator
# ts-ids-es-json-generator
## Introduction
This simple utility generates `elasticsearch.json` using `schema.json`.
## How to Use
To run the generator, simply provide the program with the location of the IDS root directory:
```
$ pipenv run python -m ids_es_json_generator ~/path/to/ids/folder
```
`elasticsearch.json` may be manually edited after automatic generation (e.g. to update non-searchable fields).
## Output
The script saves `elasticsearch.json` to the same directory you provided.
## Assumptions
The generator operates under the following assumptions:
- It's the IDS designer's responsibility to make sure the schema and `elasticsearch.json` are correct.
The generator will not validate the IDS for you.
## Restrictions
- JSON pointers are dereferenced to create the mapping, so schemas with circular references or missing references will fail.
## Tests
Run the tests using the following command:
`$ pipenv run python -m pytest`
## How it works
Anywhere an array of objects appears in the schema, a `nested` field type will be created in the output `elasticsearch.json`, including arrays of objects contained anywhere within other arrays of objects.
The steps are:
1. The schema is loaded into a dict with all of its JSON pointers dereferenced using `jsonref`, so that nested fields involving `definitions` are included in the elasticsearch mapping.
2. `find_array_object` will find all arrays of objects and create a dict representing their locations in the schema (see test case `test_array_simple_nested_case` in `__tests__/unit/test_find_array_object.py`).
3. `generate_es_mapping` uses this dict to create the elasticsearch mapping (see test case `test_nested_keys_case` in `__tests__/unit/test_generate_es_mapping.py`).
See the example files and test cases for more example usage.
%package help
Summary: Development documents and examples for ts-ids-es-json-generator
Provides: python3-ts-ids-es-json-generator-doc
%description help
# ts-ids-es-json-generator
## Introduction
This simple utility generates `elasticsearch.json` using `schema.json`.
## How to Use
To run the generator, simply provide the program with the location of the IDS root directory:
```
$ pipenv run python -m ids_es_json_generator ~/path/to/ids/folder
```
`elasticsearch.json` may be manually edited after automatic generation (e.g. to update non-searchable fields).
## Output
The script saves `elasticsearch.json` to the same directory you provided.
## Assumptions
The generator operates under the following assumptions:
- It's the IDS designer's responsibility to make sure the schema and `elasticsearch.json` are correct.
The generator will not validate the IDS for you.
## Restrictions
- JSON pointers are dereferenced to create the mapping, so schemas with circular references or missing references will fail.
## Tests
Run the tests using the following command:
`$ pipenv run python -m pytest`
## How it works
Anywhere an array of objects appears in the schema, a `nested` field type will be created in the output `elasticsearch.json`, including arrays of objects contained anywhere within other arrays of objects.
The steps are:
1. The schema is loaded into a dict with all of its JSON pointers dereferenced using `jsonref`, so that nested fields involving `definitions` are included in the elasticsearch mapping.
2. `find_array_object` will find all arrays of objects and create a dict representing their locations in the schema (see test case `test_array_simple_nested_case` in `__tests__/unit/test_find_array_object.py`).
3. `generate_es_mapping` uses this dict to create the elasticsearch mapping (see test case `test_nested_keys_case` in `__tests__/unit/test_generate_es_mapping.py`).
See the example files and test cases for more example usage.
%prep
%autosetup -n ts-ids-es-json-generator-1.0.1
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-ts-ids-es-json-generator -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.1-1
- Package Spec generated
|
