--- title: Analytics description: Parallel data management for complex queries over many records, using a familiar SQL++ syntax. pubDate: 2026-08-22T04:32:17.641Z antora: editUrl: https://github.com/couchbase/docs-sdk-cxx/edit/release/1.4/modules/howtos/pages/analytics-using-sdk.adoc xref: xref:cxx-sdk:howtos:analytics-using-sdk.adoc[] --- [Consult the llms.txt file for a full list of contents](/llms.txt) [View original HTML](/cxx-sdk/current/howtos/analytics-using-sdk.html) # Analytics > Parallel data management for complex queries over many records, using a familiar SQL++ syntax. This page covers using our operational C++ SDK to connect to the Analytics Service of a Capella Operational or self-managed Couchbase Server cluster. As well as this row-based analytics service, a speedy, column-based analytics database is available for real-time analytics. > [!TIP] > Analytics SDKs > > SDKs for [Enterprise Analytics](../../../enterprise-analytics/current/intro/intro.md) — Couchbase's analytical database for real time apps and operational intelligence (RT-OLAP) — are available for the .NET, Go, Java, Node.js, and Python platforms. See the [Enterprise Analytics SDK pages](../../../home/analytics-sdk.md) for more information. > > Currently, different SDKs are needed to connect to [Capella Analytics](../../../analytics/intro/intro.md) — as this service does not have Enterprise Analytics' load balancer, and uses a different connection protocol. Capella Analytics SDKs (also known as Columnar SDKs) are available for the Go, Java, Node.js, and Python platforms. See the [Capella Analytics SDK pages](../../../home/columnar-sdk.md) for more information. For complex and long-running queries, involving large ad hoc join, set, aggregation, and grouping operations, Couchbase Data Platform offers the [Couchbase Analytics Service (CBAS)](../../../server/current/analytics/introduction.md). This is the analytic counterpart to our [operational data focussed Query Service](sqlpp-queries-with-sdk.md). The analytics service is available in [Capella operational](../../../cloud/clusters/analytics-service/analytics-service.md)or the Enterprise Edition of self-managed Couchbase Server. ## [](#getting-started)Getting Started After familiarizing yourself with our [introductory primer](../../../server/current/analytics/primer-beer.md), in particular creating a dataset and linking it to a bucket to shadow the operational data, try Couchbase Analytics using the C++ SDK. Intentionally, the API for analytics is very similar to that of the query service. Before starting, here's all imports used in the following examples: ```c++ #include #include #include ``` Here's a complete example of doing an analytics query and handling the results: ```c++ std::string query{ R"(SELECT "hello" AS greeting)" }; auto [err, res] = cluster.analytics_query(query).get(); if (err) { fmt::println("Got an error doing analytics query: {}", err); } else { auto rows = res.rows_as(); for (const auto& row : rows) { fmt::println("row: {}", tao::json::to_string(row)); } } ``` Let's break this down. First, we get the results in the form of a `std::pair`. An `analytics_result` contains various things of interest, such as metrics, but the main thing we're interested in are the rows (results). They're fetched with a `rows_as_json` call. We check explicitly for an `error` which indicates something went wrong during the analytics query call. Please see [Error Handling](error-handling.md) for details. Here we're fetching rows converted into JSON, but as with SQL++ (formerly N1QL) there's many more options available. Rows can be returned as JSON representations from multiple third party C++ libraries, directly as a user defined class, and more. Please see [JSON Libraries](#howtos:json.adoc) for full details. Finally, we iterate through the `rows`. ## [](#queries)Queries A query can either be `simple` or be `parameterized`. If parameters are used, they can either be `positional` or `named`. Here is one example of each: ```c++ std::string query{ R"(SELECT airportname, country FROM airports WHERE country = ?)" }; auto options = couchbase::analytics_options().positional_parameters("France"); auto [err, res] = cluster.analytics_query(query, options).get(); ``` ```c++ std::string query{ R"(SELECT airportname, country FROM airports WHERE country = $country)" }; auto options = couchbase::analytics_options().named_parameters(std::pair{ "country", "France" }); auto [err, res] = cluster.analytics_query(query, options).get(); ``` ## [](#additional-parameters)Additional Parameters The handful of additional parameters are illustrated here: ```c++ std::string query{ R"(SELECT airportname, country FROM airports WHERE country = "France")" }; auto options = couchbase::analytics_options() // Ask the analytics service to give this request higher priority .priority(true) // The client context id is returned in the results, so can be used by the // application to correlate requests and responses .client_context_id("my-id") // Override how long the analytics query is allowed to take before timing out .timeout(std::chrono::milliseconds(90000)); auto [err, res] = cluster.analytics_query(query, options).get(); ``` ### [](#metadata)Metadata `analytics_result::meta_data()` contains useful metadata, such as `elapsedTime`, and `resultCount`: ```c++ std::string query{ R"(SELECT airportname, country FROM airports WHERE country = "France")" }; auto [err, res] = cluster.analytics_query(query).get(); if (err) { fmt::println("Got an error doing analytics query: {}", err); } else { auto rows = res.rows_as(); for (const auto& row : rows) { fmt::println("row: {}", tao::json::to_string(row)); } auto elapsed_time = res.meta_data().metrics().elapsed_time(); auto result_count = res.meta_data().metrics().result_count(); auto error_count = res.meta_data().metrics().error_count(); } ```