Get data with Firestore Pipeline operations

Background

Pipeline operations are a new query interface for Cloud Firestore. This interface provides advanced query functionality that includes complex expressions. It also adds support for many new functions like min, max, substring, regex_match and array_contains_all.

With Pipeline operations, index creation is also completely optional, streamlining the process of developing new queries. Pipeline operations also remove many limitations on query shape allowing you to specify large in or or queries.

Getting Started

To install and initialize client SDKs, refer to the instructions in the following guides:

Syntax

The following sections give an overview of the syntax for Pipeline operations.

Concepts

One notable difference with Pipeline operations is the introduction of explicit "stage" ordering. This makes it possible to express more complex queries. However, it is a notable deviation from the existing query interface (Core operations) where the ordering of stages was implied. Consider the following Pipeline operation example:

Web

const pipeline = db.pipeline()
  // Step 1: Start a query with collection scope
  .collection("cities")
  // Step 2: Filter the collection
  .where(field("population").greaterThan(100000))
  // Step 3: Sort the remaining documents
  .sort(field("name").ascending())
  // Step 4: Return the top 10. Note applying the limit earlier in the
  // pipeline would have unintentional results.
  .limit(10);
test.firestore.js
Swift
let pipeline = db.pipeline()
  // Step 1: Start a query with collection scope
  .collection("cities")
  // Step 2: Filter the collection
  .where(Field("population").greaterThan(100000))
  // Step 3: Sort the remaining documents
  .sort([Field("name").ascending()])
  // Step 4: Return the top 10. Note applying the limit earlier in the pipeline would have
  // unintentional results.
  .limit(10)
PipelineSnippets.swift

Kotlin

val pipeline = db.pipeline()
    // Step 1: Start a query with collection scope
    .collection("cities")
    // Step 2: Filter the collection
    .where(field("population").greaterThan(100000))
    // Step 3: Sort the remaining documents
    .sort(field("name").ascending())
    // Step 4: Return the top 10. Note applying the limit earlier in the pipeline would have
    // unintentional results.
    .limit(10)
DocSnippets.kt

Java

Pipeline pipeline = db.pipeline()
    // Step 1: Start a query with collection scope
    .collection("cities")
    // Step 2: Filter the collection
    .where(field("population").greaterThan(100000))
    // Step 3: Sort the remaining documents
    .sort(field("name").ascending())
    // Step 4: Return the top 10. Note applying the limit earlier in the pipeline would have
    // unintentional results.
    .limit(10);
DocSnippets.java
Python
from google.cloud.firestore_v1.pipeline_expressions import Field

pipeline = (
    client.pipeline()
    .collection("cities")
    .where(Field.of("population").greater_than(100_000))
    .sort(Field.of("name").ascending())
    .limit(10)
)
firestore_pipelines.py

Initialization

Pipeline operations have a very familiar syntax coming from existing Cloud Firestore queries. To get started, you initialize a query by writing the following:

Web

const { getFirestore } = require("firebase/firestore");
const { execute } = require("firebase/firestore/pipelines");
const database = getFirestore(app, "enterprise");
const pipeline = database.pipeline();
test.firestore.js
Swift
let firestore = Firestore.firestore(database: "enterprise")
let pipeline = firestore.pipeline()
PipelineSnippets.swift

Kotlin

val firestore = Firebase.firestore("enterprise")
val pipeline = firestore.pipeline()
DocSnippets.kt

Java

FirebaseFirestore firestore = FirebaseFirestore.getInstance("enterprise");
PipelineSource pipeline = firestore.pipeline();
DocSnippets.java
Python
firestore_client = firestore.client(default_app, "your-new-enterprise-database")
pipeline = firestore_client.pipeline()
firestore_pipelines.py

Structure

There are a few terms that are important to understand when creating Pipeline operations: stages, expressions, and functions.

Example demonstrating stages and expressions in a query

Stages: A pipeline may consist of one or more stages. Logically, these represent the series of steps (or stages) taken to execute the query. Note: In practice, stages may be executed out of order to improve performance. However, this does not modify the intent or correctness of the query.

Expressions: Stages will often accept an expression allowing you to express more complex queries. Expression may be simple and consist of a single function like eq("a", 1). You can also express more complex expressions by nesting expressions like and(eq("a", 1), eq("b", 2)).

Field vs. Constant References

Pipeline operations support complex expressions. As such, it may be necessary to differentiate whether a value represents a field or a constant. Consider the following example:

Web

const pipeline = db.pipeline()
  .collection("cities")
  .where(field("name").equal(constant("Toronto")));
test.firestore.js
Swift
let pipeline = db.pipeline()
  .collection("cities")
  .where(Field("name").equal(Constant("Toronto")))
PipelineSnippets.swift

Kotlin

val pipeline = db.pipeline()
    .collection("cities")
    .where(field("name").equal(constant("Toronto")))
DocSnippets.kt

Java

Pipeline pipeline = db.pipeline()
    .collection("cities")
    .where(field("name").equal(constant("Toronto")));
DocSnippets.java
Python
from google.cloud.firestore_v1.pipeline_expressions import Field, Constant

pipeline = (
    client.pipeline()
    .collection("cities")
    .where(Field.of("name").equal(Constant.of("Toronto")))
)
firestore_pipelines.py

Stages

Input Stages

The input stage represents the first stage of a query. It defines the initial set of documents you are querying over. For Pipeline operations, this largely is similar to existing queries, where most queries start with either a collection(...) or collection_group(...) stage. Two new input stages are database() and documents(...) where database() allows returning all documents in the database, while documents(...) acts identical to a batch read.

Web

let results;

// Return all restaurants in San Francisco
results = await execute(db.pipeline().collection("cities/sf/restaurants"));

// Return all restaurants
results = await execute(db.pipeline().collectionGroup("restaurants"));

// Return all documents across all collections in the database (the entire database)
results = await execute(db.pipeline().database());

// Batch read of 3 documents
results = await execute(db.pipeline().documents([
  doc(db, "cities", "SF"),
  doc(db, "cities", "DC"),
  doc(db, "cities", "NY")
]));
test.firestore.js
Swift
var results: Pipeline.Snapshot

// Return all restaurants in San Francisco
results = try await db.pipeline().collection("cities/sf/restaurants").execute()

// Return all restaurants
results = try await db.pipeline().collectionGroup("restaurants").execute()

// Return all documents across all collections in the database (the entire database)
results = try await db.pipeline().database().execute()

// Batch read of 3 documents
results = try await db.pipeline().documents([
  db.collection("cities").document("SF"),
  db.collection("cities").document("DC"),
  db.collection("cities").document("NY")
]).execute()
PipelineSnippets.swift

Kotlin

var results: Task<Pipeline.Snapshot>

// Return all restaurants in San Francisco
results = db.pipeline().collection("cities/sf/restaurants").execute()

// Return all restaurants
results = db.pipeline().collectionGroup("restaurants").execute()

// Return all documents across all collections in the database (the entire database)
results = db.pipeline().database().execute()

// Batch read of 3 documents
results = db.pipeline().documents(
    db.collection("cities").document("SF"),
    db.collection("cities").document("DC"),
    db.collection("cities").document("NY")
).execute()
DocSnippets.kt

Java

Task<Pipeline.Snapshot> results;

// Return all restaurants in San Francisco
results = db.pipeline().collection("cities/sf/restaurants").execute();

// Return all restaurants
results = db.pipeline().collectionGroup("restaurants").execute();

// Return all documents across all collections in the database (the entire database)
results = db.pipeline().database().execute();

// Batch read of 3 documents
results = db.pipeline().documents(
    db.collection("cities").document("SF"),
    db.collection("cities").document("DC"),
    db.collection("cities").document("NY")
).execute();
DocSnippets.java
Python
# Return all restaurants in San Francisco
results = client.pipeline().collection("cities/sf/restaurants").execute()

# Return all restaurants
results = client.pipeline().collection_group("restaurants").execute()

# Return all documents across all collections in the database (the entire database)
results = client.pipeline().database().execute()

# Batch read of 3 documents
results = (
    client.pipeline()
    .documents(
        client.collection("cities").document("SF"),
        client.collection("cities").document("DC"),
        client.collection("cities").document("NY"),
    )
    .execute()
)
firestore_pipelines.py

As with all other stages, the order of results from these input stages is not stable. A sort(...) operator should always be added if a specific ordering is desired.

Where

The where(...) stage acts as a traditional filter operation over documents generated from the previous stage and mostly mirrors the existing "where" syntax for existing queries. Any document for where a given expression evaluates to a non-true value is filtered out from the returned documents.

Multiple where(...) statements can be chained together, and act as an and(...) expression. For example the following two queries are logically equivalent and can be used interchangeably.

Web

let results;

results = await execute(db.pipeline().collection("books")
  .where(field("rating").equal(5))
  .where(field("published").lessThan(1900))
);

results = await execute(db.pipeline().collection("books")
  .where(and(field("rating").equal(5), field("published").lessThan(1900)))
);
test.firestore.js
Swift
var results: Pipeline.Snapshot

results = try await db.pipeline().collection("books")
  .where(Field("rating").equal(5))
  .where(Field("published").lessThan(1900))
  .execute()

results = try await db.pipeline().collection("books")
  .where(Field("rating").equal(5) && Field("published").lessThan(1900))
  .execute()
PipelineSnippets.swift

Kotlin

var results: Task<Pipeline.Snapshot>

results = db.pipeline().collection("books")
    .where(field("rating").equal(5))
    .where(field("published").lessThan(1900))
    .execute()

results = db.pipeline().collection("books")
    .where(Expression.and(field("rating").equal(5),
      field("published").lessThan(1900)))
    .execute()
DocSnippets.kt

Java

Task<Pipeline.Snapshot> results;

results = db.pipeline().collection("books")
    .where(field("rating").equal(5))
    .where(field("published").lessThan(1900))
    .execute();

results = db.pipeline().collection("books")
    .where(Expression.and(
        field("rating").equal(5),
        field("published").lessThan(1900)
    ))
    .execute();
DocSnippets.java
Python
from google.cloud.firestore_v1.pipeline_expressions import And, Field

results = (
    client.pipeline()
    .collection("books")
    .where(Field.of("rating").equal(5))
    .where(Field.of("published").less_than(1900))
    .execute()
)

results = (
    client.pipeline()
    .collection("books")
    .where(And(Field.of("rating").equal(5), Field.of("published").less_than(1900)))
    .execute()
)
firestore_pipelines.py

Select / Add & Remove Fields

The select(...), add_fields(...), & remove_fields(...) all allow you to modify the fields being returned from a previous stage. These three are generally referred to as projection-style stages.

The select(...) and add_fields(...) allow you to specify the result of an expression to a user-provided field name. An expression that results in an error will result in a null value. The select(...) will only return the documents with specified field names while add_fields(...) extends the schema of the previous stage (potentially overwriting values with identical field names).

The remove_fields(...) allows specifying a set of fields to remove from the previous stage. Specifying field names that do not exist is a no-op.

See the Restrict the Fields to Return section below but in general using such a stage to restrict the result to only the fields needed in the client is helpful in reducing the cost and latency for most queries.

Aggregate / Distinct

The aggregate(...) stage lets you perform a series of aggregations over the input documents. By default, all documents are aggregated together, but an optional grouping argument can be provided, allowing the input documents aggregated into different buckets.

Web

const results = await execute(db.pipeline()
  .collection("books")
  .aggregate(
    field("rating").average().as("avg_rating")
  )
  .distinct(field("genre"))
);
test.firestore.js
Swift
let results = try await db.pipeline()
  .collection("books")
  .aggregate([
    Field("rating").average().as("avg_rating")
  ], groups: [
    Field("genre")
  ])
  .execute()
PipelineSnippets.swift

Kotlin

val results = db.pipeline()
    .collection("books")
    .aggregate(
        AggregateStage
            .withAccumulators(AggregateFunction.average("rating").alias("avg_rating"))
            .withGroups(field("genre"))
    )
    .execute()
DocSnippets.kt

Java

Task<Pipeline.Snapshot> results = db.pipeline()
    .collection("books")
    .aggregate(AggregateStage
        .withAccumulators(
            AggregateFunction.average("rating").alias("avg_rating"))
        .withGroups(field("genre")))
    .execute();
DocSnippets.java
Python
from google.cloud.firestore_v1.pipeline_expressions import Field

results = (
    client.pipeline()
    .collection("books")
    .aggregate(
        Field.of("rating").average().as_("avg_rating"), groups=[Field.of("genre")]
    )
    .execute()
)
firestore_pipelines.py

When groupings is not specified, this stage will produce only a single document, otherwise a document will be generated for each unique combination of groupings values.

The distinct(...) stage is a simplified aggregation operator which allows generating just the unique groupings without any accumulators. It behaves identically to that of aggregate(...) in all other regards. An example is given below:

Web

const results = await execute(db.pipeline()
  .collection("books")
  .distinct(
    field("author").toUpper().as("author"),
    field("genre")
  )
);
test.firestore.js
Swift
let results = try await db.pipeline()
  .collection("books")
  .distinct([
    Field("author").toUpper().as("author"),
    Field("genre")
  ])
  .execute()
PipelineSnippets.swift

Kotlin

val results = db.pipeline()
    .collection("books")
    .distinct(
        field("author").toUpper().alias("author"),
        field("genre")
    )
    .execute()
DocSnippets.kt

Java

Task<Pipeline.Snapshot> results = db.pipeline()
    .collection("books")
    .distinct(
        field("author").toUpper().alias("author"),
        field("genre")
    )
    .execute();
DocSnippets.java
Python
from google.cloud.firestore_v1.pipeline_expressions import Field

results = (
    client.pipeline()
    .collection("books")
    .distinct(Field.of("author").to_upper().as_("author"), "genre")
    .execute()
)
firestore_pipelines.py

Functions

Functions are a building block for creating expressions and complex queries. For a complete list of functions with examples, refer to the Functions reference. As a quick reminder, consider the structure of a typical query:

Example demonstrating stages and functions in a query

Many stages accept expressions which contain one or more functions. The most common function usage will be found in the where(...) and select(...)stages. There are two main types of functions that you should be familiar with:

Web

let results;

// Type 1: Scalar (for use in non-aggregation stages)
// Example: Return the min store price for each book.
results = await execute(db.pipeline().collection("books")
  .select(field("current").logicalMinimum(field("updated")).as("price_min"))
);

// Type 2: Aggregation (for use in aggregate stages)
// Example: Return the min price of all books.
results = await execute(db.pipeline().collection("books")
  .aggregate(field("price").minimum().as("min_price"))
);
test.firestore.js
Swift
var results: Pipeline.Snapshot

// Type 1: Scalar (for use in non-aggregation stages)
// Example: Return the min store price for each book.
results = try await db.pipeline().collection("books")
  .select([
    Field("current").logicalMinimum(["updated"]).as("price_min")
  ])
  .execute()

// Type 2: Aggregation (for use in aggregate stages)
// Example: Return the min price of all books.
results = try await db.pipeline().collection("books")
  .aggregate([Field("price").minimum().as("min_price")])
  .execute()
PipelineSnippets.swift

Kotlin

var results: Task<Pipeline.Snapshot>

// Type 1: Scalar (for use in non-aggregation stages)
// Example: Return the min store price for each book.
results = db.pipeline().collection("books")
    .select(
        field("current").logicalMinimum("updated").alias("price_min")
    )
    .execute()

// Type 2: Aggregation (for use in aggregate stages)
// Example: Return the min price of all books.
results = db.pipeline().collection("books")
    .aggregate(AggregateFunction.minimum("price").alias("min_price"))
    .execute()
DocSnippets.kt

Java

Task<Pipeline.Snapshot> results;

// Type 1: Scalar (for use in non-aggregation stages)
// Example: Return the min store price for each book.
results = db.pipeline().collection("books")
    .select(
        field("current").logicalMinimum("updated").alias("price_min")
    )
    .execute();

// Type 2: Aggregation (for use in aggregate stages)
// Example: Return the min price of all books.
results = db.pipeline().collection("books")
    .aggregate(AggregateFunction.minimum("price").alias("min_price"))
    .execute();
DocSnippets.java
Python
from google.cloud.firestore_v1.pipeline_expressions import Field

# Type 1: Scalar (for use in non-aggregation stages)
# Example: Return the min store price for each book.
results = (
    client.pipeline()
    .collection("books")
    .select(
        Field.of("current").logical_minimum(Field.of("updated")).as_("price_min")
    )
    .execute()
)

# Type 2: Aggregation (for use in aggregate stages)
# Example: Return the min price of all books.
results = (
    client.pipeline()
    .collection("books")
    .aggregate(Field.of("price").minimum().as_("min_price"))
    .execute()
)
firestore_pipelines.py

Limits

For the most part Enterprise Edition doesn't impose limits on the shape of the query. In other words, you're not limited to a small number of values in an IN or OR query. Instead, there are two primary limits you should be aware of:

  • Deadline: 60 seconds (identical to Standard Edition).
  • Memory Usage: 128 MiB limit on the amount of materialized data during query execution.

Errors

You may encounter failed queries for a number of reasons. Here is a link to common errors and the associated action you can take:

Error Code Action
DEADLINE_EXCEEDED The query you are executing exceeds a 60 second deadline and requires additional optimization. See the performance section for tips. If you are unable to root cause the problem, reach out to the team.
RESOURCE_EXHAUSTED The query you are executing exceeds the memory limits and requires additional optimization. See the performance section for tips. If you are unable to root cause the problem, reach out to the team.
INTERNAL Contact the team for support.

Performance

Unlike existing queries, Pipeline operations do not require that an index is always present. This means that a query can exhibit higher latency compared to existing queries which would have just failed immediately with a FAILED_PRECONDITION missing index error. To improve the performance of Pipeline operations, there are a couple of steps you can take.

Create Indexes

Index Used

Query explain lets you identify if your query is being served by an index or falling back to a less efficient operation like a table scan. If your query is not being fully served from an index, you can create an index by following the instructions.

Creating Indexes

You can follow the existing index management documentation to create indexes. Before creating an index, familiarize yourself with general best practices with indexes in Cloud Firestore. To ensure your query can leverage indexes, follow the best practices to create indexes with fields in the following order:

  1. All fields that will be used in equality filters (in any order)
  2. All fields that will be sorted on (in the same order)
  3. Fields that will be used in range or inequality filters in decreasing order of query constraint selectivity

For example, for the following query,

Web

const results = await execute(db.pipeline()
  .collection("books")
  .where(field("published").lessThan(1900))
  .where(field("genre").equal("Science Fiction"))
  .where(field("rating").greaterThan(4.3))
  .sort(field("published").descending())
);
test.firestore.js
Swift
let results = try await db.pipeline()
  .collection("books")
  .where(Field("published").lessThan(1900))
  .where(Field("genre").equal("Science Fiction"))
  .where(Field("rating").greaterThan(4.3))
  .sort([Field("published").descending()])
  .execute()
PipelineSnippets.swift

Kotlin

val results = db.pipeline()
    .collection("books")
    .where(field("published").lessThan(1900))
    .where(field("genre").equal("Science Fiction"))
    .where(field("rating").greaterThan(4.3))
    .sort(field("published").descending())
    .execute()
DocSnippets.kt

Java

Task<Pipeline.Snapshot> results = db.pipeline()
    .collection("books")
    .where(field("published").lessThan(1900))
    .where(field("genre").equal("Science Fiction"))
    .where(field("rating").greaterThan(4.3))
    .sort(field("published").descending())
    .execute();
DocSnippets.java
Python
from google.cloud.firestore_v1.pipeline_expressions import Field

results = (
    client.pipeline()
    .collection("books")
    .where(Field.of("published").less_than(1900))
    .where(Field.of("genre").equal("Science Fiction"))
    .where(Field.of("rating").greater_than(4.3))
    .sort(Field.of("published").descending())
    .execute()
)
firestore_pipelines.py

The recommended index is a collection scope index on books for (genre [...], published DESC, avg_rating DESC).

Index density

Cloud Firestore supports sparse and non-sparse indexes. For more information, see Index density.

Covered Queries + Secondary Indexes

Cloud Firestore can skip fetching the full document and just return results from the index if all fields being returned are present in a secondary index. This normally leads to a significant latency (and cost) improvement. Using the sample query below:

Web

const results = await execute(db.pipeline()
  .collection("books")
  .where(field("category").like("%fantasy%"))
  .where(field("title").exists())
  .where(field("author").exists())
  .select(field("title"), field("author"))
);
test.firestore.js
Swift
let results = try await db.pipeline()
  .collection("books")
  .where(Field("category").like("%fantasy%"))
  .where(Field("title").exists())
  .where(Field("author").exists())
  .select([Field("title"), Field("author")])
  .execute()
PipelineSnippets.swift

Kotlin

val results = db.pipeline()
    .collection("books")
    .where(field("category").like("%fantasy%"))
    .where(field("title").exists())
    .where(field("author").exists())
    .select(field("title"), field("author"))
    .execute()
DocSnippets.kt

Java

Task<Pipeline.Snapshot> results = db.pipeline()
    .collection("books")
    .where(field("category").like("%fantasy%"))
    .where(field("title").exists())
    .where(field("author").exists())
    .select(field("title"), field("author"))
    .execute();
DocSnippets.java
Python
from google.cloud.firestore_v1.pipeline_expressions import Field

results = (
    client.pipeline()
    .collection("books")
    .where(Field.of("category").like("%fantasy%"))
    .where(Field.of("title").exists())
    .where(Field.of("author").exists())
    .select("title", "author")
    .execute()
)
firestore_pipelines.py

If the database already has a collection scope index on books for (category [...], title [...], author [...]) then it can avoid fetching anything from the main documents themselves. In this case the order in the index does not matter, [...] is used to signify that.

Restrict the Fields to Return

By default, a Cloud Firestore query returns all fields in a document, analogous to a SELECT * in traditional systems. If however your application only needs a subset of the fields, the select(...) or restrict(...) stages can be used to push this filtering server-side. This will decrease both the response size (decreasing the network egress cost) as well as improving latency.

Troubleshooting Tools

Query Explain

Query Explain lets you bring visibility in execution metrics and details about indexes used.

Metrics

Pipeline operations if fully integrated with existing Cloud Firestore metrics.

Known Issues / Limitations

Specialized Indexes

Pipeline operations do not yet support existing array-contains & vector index types. Instead of just rejecting such queries, Cloud Firestore will attempt to use other existing ascending & descending indexes. It is expected that during private preview Pipeline operations with such array_contains or find_nearest expressions will be slower than their existing equivalents due to this.

Pagination

Support for easily paginating over a result set is not supported during the private preview. This can be worked around by chaining up equivalent where(...) & sort(...) stages as shown below.

Web

// Existing pagination via `startAt()`
const q =
  query(collection(db, "cities"), orderBy("population"), startAt(1000000));

// Private preview workaround using pipelines
const pageSize = 2;
const pipeline = db.pipeline()
  .collection("cities")
  .select("name", "population", "__name__")
  .sort(field("population").descending(), field("__name__").ascending());

// Page 1 results
let snapshot = await execute(pipeline.limit(pageSize));

// End of page marker
const lastDoc = snapshot.results[snapshot.results.length - 1];

// Page 2 results
snapshot = await execute(
  pipeline
    .where(
      or(
        and(
          field("population").equal(lastDoc.get("population")),
          field("__name__").greaterThan(lastDoc.ref)
        ),
        field("population").lessThan(lastDoc.get("population"))
      )
    )
    .limit(pageSize)
);
test.firestore.js
Swift
// Existing pagination via `start(at:)`
let query = db.collection("cities").order(by: "population").start(at: [1000000])

// Private preview workaround using pipelines
let pipeline = db.pipeline()
  .collection("cities")
  .where(Field("population").greaterThanOrEqual(1000000))
  .sort([Field("population").descending()])
PipelineSnippets.swift

Kotlin

// Existing pagination via `startAt()`
val query = db.collection("cities").orderBy("population").startAt(1000000)

// Private preview workaround using pipelines
val pipeline = db.pipeline()
    .collection("cities")
    .where(field("population").greaterThanOrEqual(1000000))
    .sort(field("population").descending())
DocSnippets.kt

Java

// Existing pagination via `startAt()`
Query query = db.collection("cities").orderBy("population").startAt(1000000);

// Private preview workaround using pipelines
Pipeline pipeline = db.pipeline()
    .collection("cities")
    .where(field("population").greaterThanOrEqual(1000000))
    .sort(field("population").descending());
DocSnippets.java
Python
from google.cloud.firestore_v1.pipeline_expressions import Field

# Existing pagination via `start_at()`
query = (
    client.collection("cities")
    .order_by("population")
    .start_at({"population": 1_000_000})
)

# Private preview workaround using pipelines
pipeline = (
    client.pipeline()
    .collection("cities")
    .where(Field.of("population").greater_than_or_equal(1_000_000))
    .sort(Field.of("population").descending())
)
firestore_pipelines.py

Emulator Support

The emulator does not yet support Pipeline operations.

Realtime and Offline Support

Pipeline operations don't have realtime and offline capabilities yet.

What's next