Crack GSA SEO Indexer 1 23
Latrisha Adan
An indexer in Azure AI Search is a crawler that extracts textual data from cloud data sources and populates a search index using field-to-field mappings between source data and a search index. This approach is sometimes referred to as a 'pull model' because the search service pulls data in without you having to write any code that adds data to an index.
Indexers also drive skillset execution and AI enrichment, where you can configure skills to integrate extra processing of content en route to an index. A few examples are OCR over image files, text split skill for data chunking, text translation for multiple languages.
Indexers target supported data sources. An indexer configuration specifies a data source (origin) and a search index (destination). Several sources, such as Azure Blob Storage, have more configuration properties specific to that content type.
You can run indexers on demand or on a recurring data refresh schedule that runs as often as every five minutes. More frequent updates require a 'push model' that simultaneously updates data in both Azure AI Search and your external data source.
A search service runs one indexer job per search unit. If you need concurrent processing, make sure you have sufficient replicas. Indexers don't run in the background, so you might detect more query throttling than usual if the service is under pressure.
You should plan on creating one indexer for every target index and data source combination. You can have multiple indexers writing into the same index, and you can reuse the same data source for multiple indexers. However, an indexer can only consume one data source at a time, and can only write to a single index. As the following graphic illustrates, one data source provides input to one indexer, which then populates a single index:
Although you can only use one indexer at a time, resources can be used in different combinations. The main takeaway of the next illustration is to notice is that a data source can be paired with more than one indexer, and multiple indexers can write to same index.
Indexer connections to remote data sources can be made using standard Internet connections (public) or encrypted private connections when you use a shared private link. You can also set up connections to authenticate using a managed identity. For more information about secure connections, see Indexer access to content protected by Azure network security features and Connect to a data source using a managed identity.
On an initial run, when the index is empty, an indexer will read in all of the data provided in the table or container. On subsequent runs, the indexer can usually detect and retrieve just the data that has changed. For blob data, change detection is automatic. For other data sources like Azure SQL or Azure Cosmos DB, change detection must be enabled.
For each document it receives, an indexer implements or coordinates multiple steps, from document retrieval to a final search engine "handoff" for indexing. Optionally, an indexer also drives skillset execution and outputs, assuming a skillset is defined.
Document cracking is the process of opening files and extracting content. Text-based content can be extracted from files on a service, rows in a table, or items in container or collection. If you add a skillset and image skills, document cracking can also extract images and queue them for image processing.
When the document is a file with embedded images, such as a PDF, the indexer extracts text, images, and metadata. Indexers can open files from Azure Blob Storage, Azure Data Lake Storage Gen2, and SharePoint.
An indexer extracts text from a source field and sends it to a destination field in an index or knowledge store. When field names and data types coincide, the path is clear. However, you might want different names or types in the output, in which case you need to tell the indexer how to map the field.
Field mapping occurs after document cracking, but before transformations, when the indexer is reading from the source documents. When you define a field mapping, the value of the source field is sent as-is to the destination field with no modifications.
Skillset execution is an optional step that invokes built-in or custom AI processing. Skillsets can add optical character recognition (OCR) or other forms of image analysis if the content is binary. Skillsets can also add natural language processing. For example, you can add text translation or key phrase extraction.
If you include a skillset, you'll need to specify output field mappings in the indexer definition. The output of a skillset is manifested internally as a tree structure referred to as an enriched document. Output field mappings allow you to select which parts of this tree to map into fields in your index.
Despite the similarity in names, output field mappings and field mappings build associations from different sources. Field mappings associate the content of source field to a destination field in a search index. Output field mappings associate the content of an internal enriched document (skill outputs) to destination fields in the index. Unlike field mappings, which are considered optional, an output field mapping is required for any transformed content that should be in the index.
Indexers can offer features that are unique to the data source. In this respect, some aspects of indexer or data source configuration will vary by indexer type. However, all indexers share the same basic composition and requirements. Steps that are common to all indexers are covered below.
Indexers require a data source object that provides a connection string and possibly credentials. Data sources are independent objects. Multiple indexers can use the same data source object to load more than one index at a time.
An indexer will automate some tasks related to data ingestion, but creating an index is generally not one of them. As a prerequisite, you must have a predefined index that contains corresponding target fields for any source fields in your external data source. Fields need to match by name and data type. If not, you can define field mappings to establish the association.
An indexer definition consists of properties that uniquely identify the indexer, specify which data source and index to use, and provide other configuration options that influence run time behaviors, including whether the indexer runs on demand or on a schedule.
Any errors or warnings about data access or skillset validation will occur during indexer execution. Until indexer execution starts, dependent objects such as data sources, indexes, and skillsets are passive on the search service.
Indexers don't have dedicated processing resources. Based on this, indexers' status may show as idle before running (depending on other jobs in the queue) and run times may not be predictable. Other factors define indexer performance as well, such as document size, document complexity, image analysis, among others.
Now that you've been introduced to indexers, a next step is to review indexer properties and parameters, scheduling, and indexer monitoring. Alternatively, you could return to the list of supported data sources for more information about a specific source.
I have read in another thread that indexed data could not replicated to the new servers, so I need to wait until the retention period is reached. So far no Problem but I have a few questions about the current setup:
- We created a couple of Server classes in our deployment server, each server class has it's own outputs.conf file, where I defined the tcpout through only the old indexer. Should I have to change that to the new indexers, so all the new data will directly go through this indexers?
no problem Better to check things before do anything fatal on production. For that reason it's good to have test/lab environment where this kind of stuff can be test. Basically you could do it with trial licenses. Only issue is that you cannot use LM with trial but you could use local licenses on all nodes
Probably you need to check it from servers DB directories or (better) query from _internal that there is no replication to old peers. So put 1st those to detention then check that replication has ended. Maybe you must roll hot buckets to warm to ensure that all replication has stopped to old peers?
No need to wait data retention. You could do the next. First you should change outputs.confs point to the new peers and remove old. Then set old peers to the detention mode. After that you could remove old nodes one by one by instructions found docs (something like remove peer permanently from cluster). You must wait that first one is removed before continue with second one.
SF and RF is ok as final situation is only two peers.
One of clusters base features is rebalancing (as asked and/or when e.g. nodes restarted) buckets. This is way how you could and should transfer old buckets from old nodes to the new ones (Rebalance indexer cluster primary bucket copies).This ensure that all nodes have approximately same amount of searchable buckets. After you have rebalanced the buckets then you should take old peers away with Take a peer down permanently: the enforce-counts offline command which automatically "move" rest copies to another search peer. After all primary copies have transferred to another peer it will go down and you could decommission it. Then you should remove the second old peer.
thanks for your answers and links to the docs.
I have read the docs but I do not fully understood how it will work. Maybe you can help me again.
This is the first time I have to do such an operation to our productive systems. Therefore I like to be sure that I do not do any mistake.
This is what I understood from the docs: