query api updates (#165)

Author: terakilobyte

source/fundamentals/aggregation.txt

@@ -18,7 +18,7 @@ Overview
 In this guide, you can learn how to use **aggregation operations** in the MongoDB Java driver.
 
 Aggregation operations process data in your MongoDB collections and return computed results. MongoDB's Aggregation
-framework is modeled on the concept of data processing pipelines. Documents enter a multi-staged pipeline that
+pipeline, part of the Query API, is modeled on the concept of data processing pipelines. Documents enter a multi-staged pipeline that
 transforms the documents into an aggregated result.
 
 Another way to think of aggregation is like a car factory. Within the car factory is an assembly line, along which
@@ -117,10 +117,10 @@ Insert the Data
 Basic Aggregation Example
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
-To perform an aggregation, pass a list of aggregation stages to the 
+To perform an aggregation, pass a list of aggregation stages to the
 ``MongoCollection.aggregate()`` method.
 
-The Java driver provides the 
+The Java driver provides the
 `Aggregates <{+api+}/apidocs/mongodb-driver-core/com/mongodb/client/model/Aggregates.html>`__
 helper class that contains builders for aggregation stages.
 
@@ -163,14 +163,14 @@ Explain Aggregation Example
 To view information about how MongoDB executes your operation, use the
 ``explain()`` method of the ``AggregateIterable`` class. The ``explain()``
 method returns **execution plans** and performance statistics. An execution
-plan is a potential way MongoDB can complete an operation. 
+plan is a potential way MongoDB can complete an operation.
 The ``explain()`` method provides both the winning plan (the plan MongoDB
-executed) and rejected plans. 
+executed) and rejected plans.
 
 .. include:: /includes/fundamentals/explain-verbosity.rst
 
 In the following example, we print the JSON representation of the
-winning plans for aggregation stages that produce execution plans: 
+winning plans for aggregation stages that produce execution plans:
 
 .. literalinclude:: /includes/fundamentals/code-snippets/AggTour.java
    :language: java
@@ -183,13 +183,13 @@ The preceding code snippet should produce the following output:
 .. code-block:: none
    :copyable: false
 
-   { "stage": "PROJECTION_SIMPLE", 
+   { "stage": "PROJECTION_SIMPLE",
      "transformBy": {"stars": 1, "_id": 0},
      "inputStage": {
-      "stage": "COLLSCAN", 
+      "stage": "COLLSCAN",
       "filter": {
-         "categories": {"$eq":"bakery"}}, 
-     "direction": "forward"}} 
+         "categories": {"$eq":"bakery"}},
+     "direction": "forward"}}
 
 For more information about the topics mentioned in this section, see the
 following resources:
@@ -203,7 +203,7 @@ following resources:
 Aggregation Expression Example
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The Java driver provides builders for accumulator expressions for use with 
+The Java driver provides builders for accumulator expressions for use with
 ``$group``. You must declare all other expressions in JSON format or
 compatible document format.
 

source/fundamentals/indexes.txt

@@ -33,7 +33,7 @@ Indexes also:
 
    Indexes are also used by update operations when finding the documents to update, delete operations when finding the
    documents to delete, and by :manual:`certain stages </core/aggregation-pipeline/#pipeline-operators-and-indexes>` in
-   the aggregation framework.
+   the aggregation pipeline.
 
 Query Coverage and Performance
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

source/fundamentals/time-series.txt

@@ -14,7 +14,7 @@ Overview
 --------
 
 In this guide, you can learn about **time series collections** in
-MongoDB, and how to interact with them in the MongoDB Java driver. 
+MongoDB, and how to interact with them in the MongoDB Java driver.
 
 Time series collections efficiently store sequences of measurements over
 a period of time. Time series data consists of any data collected over
@@ -26,7 +26,7 @@ measurement.
    :header-rows: 1
    :stub-columns: 1
 
-   * - Example 
+   * - Example
      - Measurement
      - Metadata
 
@@ -43,7 +43,7 @@ Create a Time Series Collection
 
 To create a time series collection, pass the following parameters to the
 `createCollection() <{+api+}/apidocs/mongodb-driver-sync/com/mongodb/client/MongoDatabase.html#createCollection(java.lang.String,com.mongodb.client.model.CreateCollectionOptions)>`__
-method: 
+method:
 
 - The name of the new collection to create
 - The `TimeSeriesOptions <{+api+}/apidocs/mongodb-driver-core/com/mongodb/client/model/TimeSeriesOptions.html>`__  for creating the collection in a `CreateCollectionOptions <{+api+}/apidocs/mongodb-driver-core/com/mongodb/client/model/CreateCollectionOptions.html>`__ object
@@ -96,12 +96,11 @@ Query a Time Series Collection
 
 To query in a time series collection, use the same conventions as you
 would for :doc:`retrieving </fundamentals/crud/read-operations/retrieve>`
-and :ref:`aggregating data <java-aggregation>`. 
+and :ref:`aggregating data <java-aggregation>`.
 
 .. note:: Window Functions
 
    MongoDB version 5.0 introduces window functions into the aggregation
-   framework. You can use window functions to perform operations on a
+   pipeline. You can use window functions to perform operations on a
    contiguous span of time series data. For more information, see our
    :ref:`Aggregates Builders guide <builders-aggregates-setWindowFields>`.
-