Data import overview - Azure Search
In Azure Search, queries execute over your content loaded into and saved in a search index. This article examines the two basic approaches for populating an index: push your data into the index programmatically, or point an Azure Search indexer at a supported data source to pull in the data.
With either approach, the objective is to load data from an external data source into an Azure Search index. Azure Search will let you create an empty index, but until you push or pull data into it, it's not queryable.
Pushing data to an index
The push model, used to programmatically send your data to Azure Search, is the most flexible approach. First, it has no restrictions on data source type. Any dataset composed of JSON documents can be pushed to an Azure Search index, assuming each document in the dataset has fields mapping to fields defined in your index schema. Second, it has no restrictions on frequency of execution. You can push changes to an index as often as you like. For applications having very low latency requirements (for example, if you need search operations to be in sync with dynamic inventory databases), the push model is your only option.
This approach is more flexible than the pull model because you can upload documents individually or in batches (up to 1000 per batch or 16 MB, whichever limit comes first). The push model also allows you to upload documents to Azure Search regardless of where your data is.
How to push data to an Azure Search index
You can use the following APIs to load single or multiple documents into an index:
There is currently no tool support for pushing data via the portal.
For an introduction to each methodology, see Quickstart: Create an Azure Search index using PowerShell and the REST API or Quickstart: Create an Azure Search index in C#.
Indexing actions: upload, merge, mergeOrUpload, delete
You can control the type of indexing action on a per-document basis, specifying whether the document should be uploaded in full, merged with existing document content, or deleted.
In the REST API, issue HTTP POST requests with JSON request bodies to your Azure Search index's endpoint URL. Each JSON object in the "value" array contains the document's key and specifies an indexing action adds, updates, or deletes document content. For a code example, see Load documents.
In the .NET SDK, package up your data into an
IndexBatch object. An
IndexBatch encapsulates a collection of
IndexAction objects, each of which contains a document and a property that tells Azure Search what action to perform on that document. For a code example, see Construct IndexBatch.
|@search.action||Description||Necessary fields for each document||Notes|
||key, plus any other fields you wish to define||When updating/replacing an existing document, any field that is not specified in the request will have its field set to
||Updates an existing document with the specified fields. If the document does not exist in the index, the merge will fail.||key, plus any other fields you wish to define||Any field you specify in a merge will replace the existing field in the document. In the .NET SDK, this includes fields of type
||This action behaves like
||key, plus any other fields you wish to define||-|
||Removes the specified document from the index.||key only||Any fields you specify other than the key field will be ignored. If you want to remove an individual field from a document, use
Decide which indexing action to use
To import data using the .NET SDK, (upload, merge, delete, and mergeOrUpload). Depending on which of the below actions you choose, only certain fields must be included for each document:
Formulate your query
There are two ways to search your index using the REST API. One way is to issue an HTTP POST request where your query parameters are defined in a JSON object in the request body. The other way is to issue an HTTP GET request where your query parameters are defined within the request URL. POST has more relaxed limits on the size of query parameters than GET. For this reason, we recommend using POST unless you have special circumstances where using GET would be more convenient.
For both POST and GET, you need to provide your service name, index name, and the proper API version (the current API version is
2017-11-11 at the time of publishing this document) in the request URL. For GET, the query string at the end of the URL is where you provide the query parameters. See below for the URL format:
https://[service name].search.windows.net/indexes/[index name]/docs?[query string]&api-version=2017-11-11
The format for POST is the same, but with only api-version in the query string parameters.
Pulling data into an index
The pull model crawls a supported data source and automatically uploads the data into your index. In Azure Search, this capability is implemented through indexers, currently available for these platforms:
Indexers connect an index to a data source (usually a table, view, or equivalent structure), and map source fields to equivalent fields in the index. During execution, the rowset is automatically transformed to JSON and loaded into the specified index. All indexers support scheduling so that you can specify how frequently the data is to be refreshed. Most indexers provide change tracking if the data source supports it. By tracking changes and deletes to existing documents in addition to recognizing new documents, indexers remove the need to actively manage the data in your index.
How to pull data into an Azure Search index
An advantage to using the portal is that Azure Search can usually generate a default index schema for you by reading the metadata of the source dataset. You can modify the generated index until the index is processed, after which the only schema edits allowed are those that do not require reindexing. If the changes you want to make impact the schema directly, you would need to rebuild the index.
Verify data import with Search explorer
A quick way to perform a preliminary check on the document upload is to use Search explorer in the portal. The explorer lets you query an index without having to write any code. The search experience is based on default settings, such as the simple syntax and default searchMode query parameter. Results are returned in JSON so that you can inspect the entire document.
Numerous Azure Search code samples include embedded or readily available datasets, offering an easy way to get started. The portal also provides a sample indexer and data source consisting of a small real estate dataset (named "realestate-us-sample"). When you run the preconfigured indexer on the sample data source, an index is created and loaded with documents that can then be queried in Search explorer or by code that you write.
Send feedback about: