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435719c65b
This starts the concept of a schema checker, similar to zerver/lib/validator.py on the server. We can use this to validate incoming data. Our server should filter most of our incoming data, but it's useful to have client-side checking to defend against things like upgrade regressions (i.e. what if we change the name of the field on the server side without updating all client uses).
60 lines
1.2 KiB
JavaScript
60 lines
1.2 KiB
JavaScript
zrequire('schema');
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run_test('basics', () => {
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assert.equal(schema.check_string('x', 'fred'), undefined);
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assert.equal(schema.check_string('x', [1,2]), 'x is not a string');
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const fields = {
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foo: schema.check_string,
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bar: schema.check_string,
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};
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const check_rec = (val) => {
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return schema.check_record('my_rec', val, fields);
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};
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assert.equal(
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check_rec({foo: 'apple', bar: 'banana'}),
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undefined
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);
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assert.equal(
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check_rec('bogus'),
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'my_rec is not a record'
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);
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assert.equal(
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check_rec({foo: 'apple'}),
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'in my_rec bar is missing'
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);
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assert.equal(
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check_rec({}),
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'in my_rec bar is missing, foo is missing'
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);
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assert.equal(
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check_rec({foo: 'apple', bar: 42}),
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'in my_rec bar is not a string'
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);
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const check_array = (val) => {
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return schema.check_array('lst', val, schema.check_string);
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};
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assert.equal(
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check_array(['foo', 'bar']),
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undefined
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);
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assert.equal(
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check_array('foo'),
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'lst is not an array'
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);
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assert.equal(
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check_array(['foo', 3]),
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'in lst we found an item where item is not a string'
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);
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});
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