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A speedy LookML parser implemented in pure Python.

Project description

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lkml

A speedy LookML parser and serializer implemented in pure Python.

lkml.load parses LookML strings to Python objects or JSON strings. lkml.dump serializes (generates) LookML strings from Python objects.

Why should you use lkml?

  • Tested on over 160K lines of LookML from public repositories on GitHub
  • Parses a typical view or model file in < 10 ms (excludes I/O time)
  • Written in pure, modern Python 3.7 with no external dependencies
  • A full unit test suite with excellent coverage

Interested in contributing to lkml? Check out the contributor guidelines.

How do I install it?

lkml is available to install on pip via the following command:

pip install lkml

How do I run it?

You can run lkml from the command line (parsing only) or import it as a Python package (parsing and serializing).

lkml uses a similar interface as the json and yaml Python packages. The package has two functions:

  • load, which accepts a file object and returns a dictionary with the parsed result
  • dump, which accepts a Python dictionary and an optional file object to write to. If no file object is provided, dump returns the serialized string directly.

How does lkml represent LookML in Python?

lkml represents LookML as a nested dictionary structure in Python. Within this documentation, we'll refer to LookML field names (e.g. sql_table_name, view, join) as keys.

During parsing,

  • Blocks with keys like dimension and view become dictionaries. lkml adds a key called name if the block has a name (e.g. the name of the dimension or view)
  • Keys with literal values like hidden: yes become keys and values {"hidden": "yes"} in their parent dictionaries
  • Lists (e.g. fields) become lists in their parent dictionaries

A number of LookML keys can be repeated, like dimension, include, or view. lkml collects these repeated keys into lists with a pluralized key (e.g. dimension becomes dimensions).

Here's an example of some LookML that has been parsed into a dictionary. Note that the repeated key join has been transformed into a plural key joins: a list of dictionaries representing each join.

{
  "connection": "connection_name",
  "explores": [
    {
      "label": "Explore",
      "joins": [
        {
          "relationship": "many_to_one",
          "type": "inner",
          "sql_on": "${view_one.dimension} = ${view_two.dimension}",
          "name": "view_two"
        },
        {
          "relationship": "one_to_many",
          "type": "inner",
          "sql_on": "${view_one.dimension} = ${view_three.dimension}",
          "name": "view_three"
        }
      ],
      "name": "view_one"
    },
  ]
}

Parsing LookML in Python

Parsing LookML in Python is simple with lkml. Imagine the view below.

view: view_name {
  sql_table_name: analytics.orders ;;

  dimension: order_id {
    primary_key: yes
    type: number
    sql: ${TABLE}.order_id ;;
  }
}

lkml.load accepts a file object or a LookML string and returns the parsed result as a dictionary. Here we pass it a file object.

import lkml

with open('path/to/file.view.lkml', 'r') as file:
    parsed = lkml.load(file)

load returns this dictionary.

{
  "views": [
    {
      "sql_table_name": "analytics.orders",
      "dimensions": [
        {
          "primary_key": "yes",
          "type": "number",
          "sql": "${TABLE}.order_id",
          "name": "order_id",
        }
      ],
      "name": "view_name"
    }
  ]
}

Notice how the name of the dimension, order_id, is preserved in the name key of the first element of the list value of dimensions. Similarly, the name of the view is also preserved.

Serializing (generating) LookML in Python

lkml.dump accepts a Python dictionary representing the LookML that you would like to generate. If you pass a file object as an input argument, it will write the serialized result to that file. If not, it returns a LookML string.

lkml does not validate the LookML it generates. lkml.dump's only standard is that the serialized output could be successfully parsed by lkml.load. It's entirely possible to generate invalid LookML if the input is malformed. For help representing the input object appropriately, see the section on representing LookML in Python above.

lkml descends through the dictionary, writing LookML based on the keys and values it finds.

  • If the value is a dictionary, lkml creates a block. Dictionaries can have an optional key called name (in this case, the name of this dimension is price), as well as a number of key/value pairs. To name a block, include the name key in the dictionary to be serialized. Here's an example of a dictionary we might provide to lkml.dump.

    {
      "dimension": {
        "type": "number",
        "label": "Unit Price",
        "sql": "${TABLE}.price",
        "name": "price"
      }
    }
    

    And here's the resulting block of LookML that is generated.

    dimension: price {
      type: number
      label: "Unit Price"
      sql: ${TABLE}.price ;;
    }
    
  • If the value is a list, lkml checks the key against a list of known repeatable keys. In the example above, we used a nested dictionary to represent a dimension block. However, LookML allows multiple blocks with the same key (e.g. dimension, view, set, etc.). Since Python dictionaries cannot have duplicate keys, we represent these repeated keys in our dictionary as a single key/value pair, where the key is a pluralized version of the original key (dimensions instead of dimension), and the value is a list of objects that represent each individual field.

    For example, multiple joins on an explore should be represented as follows.

    "joins": [
      {
        "relationship": "many_to_one",
        "type": "inner",
        "sql_on": "${view_one.dimension} = ${view_two.dimension}",
        "name": "view_two"
      },
      {
        "relationship": "one_to_many",
        "type": "inner",
        "sql_on": "${view_one.dimension} = ${view_three.dimension}",
        "name": "view_three"
      }
    ]
    

    If the key is not in the list of known repeated keys, lkml creates a list. Here's an example of a list in LookML.

    fields: [orders.price, orders.ordered_date, orders.order_id]
    
  • If the value is a string, lkml creates a quoted or unquoted string based on the key. For example, the value for label would be quoted, but the value for hidden would not. Values with keys like sql_table_name or html that indicate an expression automatically have a trailing space and ;; appended.

Let's say we've parsed the example view from "Parsing LookML in Python" above. We've parsed it into a dictionary and now we want to modify it. We want to change the type of the dimension order_id from number to string. Using lkml, it's easy to modify the value of type in Python and dump it to LookML.

First, we'll modify the value of type in the parsed dictionary.

parsed['views'][0]['dimensions'][0]['type'] = 'string'

Next, we'll dump the dictionary back to LookML in a new file.

with open('path/to/new.view.lkml', 'w+') as file:
    lkml.dump(parsed, file)

Here's the output.

view: {
  sql_table_name: analytics.orders ;;

  dimension: order_id {
    primary_key: yes
    type: string
    sql: ${TABLE}.order_id ;;
  }
}

Parsing LookML from the command line

At the command line, lkml accepts a single positional argument: the path to the LookML file to parse. It returns the parsed result to stdout as a JSON string.

Here's an example.

lkml path/to/file.view.lkml

If you would like to save the result to a file, you can pipe the output as follows.

lkml path/to/file.view.lkml > path/to/result.json

When running from the command line, pass the debug flag (-d or --debug) to observe how the parser is attempting to navigate and parse the file.

lkml path/to/file.view.lkml --debug

The debug statements indicate how the parser is descending through the LookML, expecting certain grammar (e.g. [pair] = key value), and checking tokens against the expected grammar.

lkml.parser . Try to parse [pair] = key value
lkml.parser . . Try to parse [key] = literal ':'
lkml.parser . . . Check LiteralToken(type) == LiteralToken
lkml.parser . . . Check ValueToken() == ValueToken
lkml.parser . . Successfully parsed key.
lkml.parser . . Try to parse [value] = literal / quoted_literal / expression_block
lkml.parser . . . Check LiteralToken(full_outer) == QuotedLiteralToken or LiteralToken
lkml.parser . . Successfully parsed value.
lkml.parser . Successfully parsed pair.

How does it work?

lkml is made up of three components, a lexer, a parser, and a serializer. The parser is a recursive descent parser with backtracking.

First, the lexer scans through the input string character by character and generates a stream of relevant tokens. The lexer skips over whitespace when it's not relevant.

For example, the input string:

"sql: ${TABLE}.order_date ;;"

would be broken into the tuple of tokens:

(
    LiteralToken(sql),
    ValueToken(),
    ExpressionBlockToken(${TABLE}.order_date),
    ExpressionBlockEndToken()
)

Next, the parser scans through the stream of tokens. It marks its position in the stream, then attempts to identify a matching rule in the grammar. If the rule is made up of other rules (this is a called a non-terminal), it descends recursively through the constituent rules looking for tokens that match.

If it doesn't find a match for a rule, it backtracks to a previously marked point in the stream and tries the next available rule. If the parser runs out of rules to try, it raises a syntax error.

As the parser finds matches, it adds the relevant token values to its syntax tree, which is eventually returned to the user if the input parses successfully.

To dump LookML to a string, lkml calls the serializer, which navigates through the Python dictionary provided, writing out blocks, sets, pairs, keys, and values where needed.

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