Module yaml

Overview

The yaml module takes strings in YAML format and decodes them, or takes a series of non-YAML values and encodes them.

Index

Below is a list of all yaml functions and members.

yaml.encode(lua_value)¶

Convert a Lua object to a YAML string.

Parameters:	lua_value – either a scalar value or a Lua table value.
Return:	the original value reformatted as a YAML string.
Rtype:	string

yaml.decode(string)¶

Convert a YAML string to a Lua object.

Parameters:	string – a string formatted as YAML.
Return:	the original contents formatted as a Lua table.
Rtype:	table

__serialize parameter:

The YAML output structure can be specified with __serialize:

• 'seq', 'sequence', 'array': table encoded as an array
• 'map', 'mapping': table encoded as a map
• function: the meta-method called to unpack serializable representation of table, cdata, or userdata objects

tarantool> yaml.encode(setmetatable({'A', 'B'}, {__serialize='seq'}))
---
- |
  --- ['A', 'B']
  ...
...
tarantool> yaml.encode(setmetatable({'A', 'B'}, {__serialize='map'}))
---
- |
  --- {1: 'A', 2: 'B'}
  ...
...

'seq' or 'map' also enable the flow (compact) mode for the YAML serializer (flow="[1,2,3]" vs block=" - 1\n - 2\n - 3\n"). See the full example in the ‘Example’ section below.

yaml.cfg(table)¶

Set values affecting the behavior of encode and decode functions.

The values are all either integers or boolean true/false.

Option	Default	Use
cfg.encode_invalid_numbers	true	A flag saying whether to enable encoding of NaN and Inf numbers
cfg.encode_number_precision	14	Precision of floating point numbers
cfg.encode_load_metatables	true	A flag saying whether the serializer will follow __serialize metatable field
cfg.encode_use_tostring	false	A flag saying whether to use tostring() for unknown types
cfg.encode_invalid_as_nil	false	A flag saying whether to use NULL for non-recognized types
cfg.encode_sparse_convert	true	A flag saying whether to handle excessively sparse arrays as maps. See detailed description below
cfg.encode_sparse_ratio	2	1/encode_sparse_ratio is the permissible percentage of missing values in a sparse array
cfg.encode_sparse_safe	10	A limit ensuring that small Lua arrays are always encoded as sparse arrays (instead of generating an error or encoding as map)
cfg.decode_invalid_numbers	true	A flag saying whether to enable decoding of NaN and Inf numbers
cfg.decode_save_metatables	true	A flag saying whether to set metatables for all arrays and maps

Note on ``decode_save_metatables``

You may want to change the result’s metatable to get block-formatted encode() for better readability, but be careful to do it correctly.

The correct way is to assign a new metatable.

tarantool> t1 = yaml.decode(yaml.encode({[1] = 'a', x = 'b'}))
tarantool> yaml.encode(t1)
---
- |
  --- {'x': 'b', 1: 'a'}
  ...
...
tarantool> my_mt = {__serialize = 'mapping'}
tarantool> setmetatable(t1, my_mt)
tarantool> yaml.encode(t1)
---
- |
  ---
  x: b
  1: a
  ...
...

Do not change the metatable like this.

tarantool> t1 = yaml.decode(yaml.encode({[1] = 'a', x = 'b'}))
tarantool> getmetatable(t1).__serialize
---
- map
...
tarantool> getmetatable(t1).__serialize = 'mapping' -- (!) bad
tarantool> t2 = yaml.decode(yaml.encode({[1] = 'a', x = 'b'}))
tarantool> yaml.encode(t2) -- (!) got 'block' maps for all results
---
- |
  ---
  x: b
  1: a
  ...
...

Sparse arrays features:

During encoding, The YAML encoder tries to classify table into one of four kinds:

• Map: at least one table index is not unsigned integer.
• Regular array: all array indexes are available.
• Sparse array: at least one array index is missing.
• Excessively sparse array: the number of values missing exceeds the configured ratio.

An array is excessively sparse when all the following conditions are met:

• encode_sparse_ratio > 0
• max(table) > encode_sparse_safe
• max(table) > count(table) * encode_sparse_ratio

The YAML encoder will never consider an array to be excessively sparse when encode_sparse_ratio = 0. The encode_sparse_safe limit ensures that small Lua arrays are always encoded as sparse arrays. By default, attempting to encode an excessively sparse array will generate an error. If encode_sparse_convert is set to true, excessively sparse arrays will be handled as maps.

yaml.cfg() example 1:

The following code will encode 0/0 as NaN (“not a number”) and 1/0 as Inf (“infinity”), rather than returning nil or an error message:

yaml = require('yaml')
yaml.cfg{encode_invalid_numbers = true}
x = 0/0
y = 1/0
yaml.encode({1, x, y, 2})

The result of the yaml.encode() request will look like this:

tarantool> yaml.encode({1, x, y, 2})
---
- '[1,nan,inf,2]
...

yaml.cfg example 2:

To avoid generating errors on attempts to encode unknown data types as userdata/cdata, you can use this code:

tarantool> httpc = require('http.client').new()
---
...

tarantool> yaml.encode(httpc.curl)
---
- error: unsupported Lua type 'userdata'
...

tarantool> yaml.encode(httpc.curl, {encode_use_tostring=true})
---
- '"userdata: 0x010a4ef2a0"'
...

Similar configuration settings exist for JSON and MsgPack.

yaml.NULL¶

A value comparable to Lua “nil” which may be useful as a placeholder in a tuple.

Example

tarantool> yaml = require('yaml')
---
...
tarantool> y = yaml.encode({'a', 1, 'b', 2})
---
...
tarantool> z = yaml.decode(y)
---
...
tarantool> z[1], z[2], z[3], z[4]
---
- a
- 1
- b
- 2
...
tarantool> if yaml.NULL == nil then print('hi') end
hi
---
...

The YAML collection style can be specified with __serialize:

• __serialize="sequence" or __serialize="array" for a Block Sequence array,
• __serialize="seq" for a Flow Sequence array,
• __serialize="mapping" for a Block Mapping map,
• __serialize="map" for a Flow Mapping map.

Serializing array- or map-like tables containing 'A' and 'B' with different __serialize values brings different results:

tarantool> yaml = require('yaml')
---
...
tarantool> yaml.encode(setmetatable({'A', 'B'}, {__serialize='seq'}))
---
- |
  --- ['A', 'B']
  ...
...
tarantool> yaml.encode(setmetatable({'A', 'B'}, {__serialize='map'}))
---
- |
  --- {1: 'A', 2: 'B'}
  ...
...
tarantool> array_like_table = {'A', 'B'}
tarantool> yaml.encode(setmetatable(array_like_table, {__serialize='seq'}))
---
- |
  --- ['A', 'B']
  ...
...
tarantool> yaml.encode(setmetatable(array_like_table, {__serialize='sequence'}))
tarantool> yaml.encode(setmetatable(array_like_table, {__serialize='array'}))
---
- |
  ---
  - A
  - B
  ...
...
tarantool> yaml.encode(setmetatable(array_like_table, {__serialize='map'}))
---
- |
  --- {1: 'A', 2: 'B'}
  ...
...
tarantool> yaml.encode(setmetatable(array_like_table, {__serialize='mapping'}))
---
- |
  ---
  1: A
  2: B
  ...
...
tarantool> map_like_table = {f1 = 'A', f2 = 'B'}
tarantool> yaml.encode(setmetatable(map_like_table, {__serialize='seq'}))
tarantool> yaml.encode(setmetatable(map_like_table, {__serialize='sequence'}))
tarantool> yaml.encode(setmetatable(map_like_table, {__serialize='array'}))
---
- |
  --- []
  ...
...
tarantool> yaml.encode(setmetatable(map_like_table, {__serialize='map'}))
---
- |
  --- {'f2': 'B', 'f1': 'A'}
  ...
...
tarantool> yaml.encode(setmetatable(map_like_table, {__serialize='mapping'}))
---
- |
  ---
  f2: B
  f1: A
  ...
...