es-alchemy

ESAlchemy

Simplification of Elasticsearch interactions

Install

npm i --save es-alchemy

Setup

Outline of how ESAlchemy can be used, step by step:

• Define data models
• Define indices based on the data models
• Generate (current version) mappings from indices and then create them in Elasticsearch
• Obtain input data as defined in the source mappings of index and remap it
• Insert remapped data into Elasticsearch
• Build a query using the ES-Alchemy query syntax
• Run query against Elasticsearch

Model and Index Definitions

Models

Models definitions contain the fields of a model and their types. They restrict how an index can be put together.

Example: address.json

{
  "fields": {
    "id": "uuid",
    "street": "string",
    "city": "string",
    "country": "string",
    "centre": "point",
    "area": "shape",
    "timezone": "string"
  }
}

Preferably a folder models contains a json file for each model. An example can be found in the test folder.

Fields that can be used and how they get mapped in Elasticsearch can be found here.

Indices

Indices define how data, models and mappings all tie together.

Example: location.json

{
  "model": "location",
  "fields": [
    "id",
    "name"
  ],
  "nested": {
    "address": {
      "model": "address",
      "fields": [
        "id",
        "street",
        "city",
        "country",
        "centre",
        "area",
        "timezone"
      ],
      "sources": [
        "address"
      ]
    }
  }
}

Preferably a folder indices contains a json file for each index. An example can be found in the test folder.

Each index is defined as a nested structure of nodes. Nodes are defined recursively and each node has the following fields:

version

Type: string
Default: null

Defines the version of this index. Should only be defined on root node.

model

Type: string
Required.

Model that is used for the node.

Can append [] for non-root nodes to indicate to-many relationship.

fields

Type: Array
Required.

Fields of the node model that are included in this index. Needs to be a subset of the fields defined on the model.

sources

Type: Array
Default: [""]

Defines the relative sources for data ingestion. How this works is explained under data ingestion and remapping.

nested

Type: Object
Default: {}

Defines all children of the node. Keys indicate the relationship names and the values define the nodes.

To indicate that a to-many relationship is defined, append [] to the model name in the node.

flat

Type: boolean
Default: false

This flag sets include_in_root to true on the generated Elasticsearch mapping. Internally in Elasticsearch this means all fields get flattened into the root document of the mapping.

This is useful to reduce storage size by de-duplicating and to enforce union target style queries (see corresponding section for more on this).

Loading Models and Indices

Loading models and indices into ES-Alchemy can be done as following. Note that an index can only be registered once all models used in the index have been registered.

const ESA = require("es-alchemy");

const esa = ESA({ endpoint: "localhost:9200" });

Object.entries(ESA.loadJsonInDir("path/to/models"))
  .forEach(([name, specs]) => esa.model.register(name, specs));
Object.entries(ESA.loadJsonInDir("path/to/indices"))
  .forEach(([name, specs]) => esa.index.register(name, specs));

Generating Mappings

Mappings can be obtained from indices by calling:

esa.index.getMapping("indexName");

To (re)create a mapping in Elasticsearch run:

Promise.all(esa.index.list().map(idx => esa.rest.mapping.recreate(idx)))
  .then(() => {
    // ...
  });

Remapping and Ingesting Data

To insert data into Elasticsearch we need a source object. Data is then extracted from this source object and remapped into a target object that can be ingested into Elasticsearch.

To define how the source object gets remapped, the sources fields in the nodes of the index are used.

Multiple sources for a node can be defined. This is really convenient when mappings e.g. keywords from multiple entities in the source object to a single keywords relationship on the index.

When an index relationship maps to a single model (to-one), it is expected that no more than one model is retrieved.

Models and their fields are picked up from all paths defined in the sources Array. An empty string indicates root level. If a field is missing from a source, it is skipped. Unexpected fields are skipped.

Sources are defined relative to their parent sources.

Known limitations:

• Fields can not be renamed when remapping

To remap and ingest data run

sourceObject = {/* ... */};
esa.rest.data
  .update("indexName", { upsert: [esa.data.remap("indexName", sourceObject)] });

// todo: this needs an example

Building Queries

To query data in Elasticsearch we first need to build a query. This is done using the ESAlchemy query syntax.

List of all available commands for filterBy, orderBy and scoreBy can be found here.

When building a query the following options are available.

toReturn

Type: Array
Default: [""]

Indicates which fields should be returned in the result. Nested fields can be accessed using dot notation.

filterBy

Type: Array|Object
Default: []

Allow restriction of results.

Pass in object containing an or or and key, mapping to a list of filter options.

A filter option is either a similarly structured object, a filter array or a string (shorthand notation).

When and is used, a target key can also be present with the values separate or union. This option only takes effect when multiple clauses search the same relationship. When separate is used, all conditions need to be met on the same object. When union is used, they can be met on different objects in the relationship.

// todo: this section needs improvement

orderBy

Type: Array
Default: []

Allow ordering of results. Takes precedence over scoring.

scoreBy

Type: Array
Default: []

Allow scoring of results. Ordering takes precedence over scoring.

limit

Type: Integer
Default: 20

Maximum amount of results returned.

offset

Type: Integer
Default: 0

Results to skip at the beginning of the results.

Constructor

protocol

Type: string
Default: http

The protocol for connecting to Elasticsearch. Can be http or https.

endpoint

Type: string
Default: elasticsearch:9200

The endpoint for connecting to Elasticsearch. Common values include localhost:9200.

aws

Type: object
Default: {}

Allow connection to AWS Elasticsearch instance by passing in object containing accessKeyId and secretAccessKey.

Index Versions

Indices are versioned using a computed hash deduced from their schema. So an index named foo uses multiple mappings as foo@HASH under the hood. When updating or deleting a document the document is removed from all old version and updated in the current version as required. Empty, old version are removed.

When the version of an index changes the new index mapping needs to be created. Calling mapping.create on every initialization should be ok to do.

Api

Available commands

model

• register(name: String, definition: Object) - register a model with ES-Alchemy

index

• register(name: String, definitions: Object) - register an index with ES-Alchemy
• list() - list all indices registered with ES-Alchemy
• getMapping(name: String) - get the mapping for Elasticsearch for this index
• getFields(name: String) - get all fields (including nested) for this index
• getRels(name: String) - get all rels for this index (returned as object mapping to node type)
• getModel(name: String) - get top level model of this index

data

• remap(name: String, source: Object) - remap source object to ingestible object, see details in corresponding section

query

• build(name: String?, options: Object) - build a query, index is optional and can be passed as null, see details in corresponding section

rest

Interacting with the rest api of Elasticsearch

• call(method: String, name: String, options: Object) - make direct API call to Elasticsearch
• mapping.create(name: String) - create mapping on Elasticsearch (call when version changes)
• mapping.delete(name: String) - delete mapping from Elasticsearch (deletes all versions)
• mapping.get(name: String) - get mapping details from Elasticsearch (current version)
• mapping.historic(name: String) - get old mapping versions and their respective document counts from Elasticsearch
• mapping.recreate(name: String) - recreate mapping on Elasticsearch (deletes all versions and recreates current version)
• data.count(name: String) - get number of indexed elements from Elasticsearch (from all versions)
• data.query(name: String, filter: Object, options: Object) - query for data in Elasticsearch against all versions. Use raw flag to obtain raw result from Elasticsearch.
• data.refresh(name: String) - refresh Elasticsearch index, useful e.g. when testing (all versions)
• data.historic(limit: Integer = 100) - fetch historic data entries as { [ID]: [INDEX] }. Order of results is random.
• data.update(name: String, options: Object) - insert, update or delete objects in Elasticsearch (current version, removed touched documents from old versions and deletes old versions when empty)

Tests

All tests need to be run in docker. Start with:

. manage.sh

To test Elasticsearch works correctly, run

curl http://elasticsearch:9200/_cluster/health

Run all tests with

npm t