Search the Community

Developers love Firestore because of how fast they can build applications and services end to end. As of today, Firestore has more than 500,000 monthly active developers, and Firestore apps power more than 1.3 billion monthly active end users using Firebase Auth. Recently, we’ve made updates to Firestore to improve developer productivity, enable developers to build the next generation AI-enabled applications, express richer queries, and help ensure that Firestore databases meet enterprises’ ever-growing needs. Using Gemini to build applications with Firestore We’re excited to share that Gemini Code Assist now incorporates assistive capabilities for developing with Firestore. Using Gemini Code Assist, along with your favorite Integrated Development Environment (IDE), you can use natural language to define your Firestore data models and write queries. For example, you can express queries using natural language statements like “get products from Firestore inventory collection” and translate that into Firestore SDK code in your favorite programming language: Use natural language to write a Firestore query with Gemini Code Assist To get started, refer to the Gemini Code Assist documentation to install the plugin for your favorite IDE, including Visual Studio Code, IntelliJ or Cloud Code. You can also use Gemini to generate solution architectures and provision Firestore resources. Simply use the Gemini in Cloud Run application canvas in private preview, and type in a prompt like “LangChain app with Firestore and Vertex.” Use natural language using Gemini in Cloud Run application canvas, to generate a solution architecture that includes Firestore You can learn more about the Gemini in Cloud Run application canvas here. Build next-gen AI-enabled applications If you’re trying to build AI-enabled solutions such as a chatbot or recommendations engine, Firestore has you covered. Firestore now has built-in support for vector search using exact nearest neighbors, the ability to automatically generate vector embeddings using popular embedding models via a turn-key extension, and integrations with popular generative AI libraries such as LangChain and LlamaIndex. Here’s an example of using Firestore’s vector search capabilities: code_block <ListValue: [StructValue([('code', 'collection_ref = collection(‘beans’)\r\ncollection_ref\r\n.where("type", "==", "arabica")\r\n.find_nearest(\r\n vector_field="embedding_field",\r\n query_vector=Vector([0.1, 0.2, …, 1]),\r\n distance_measure=DistanceMeasure.COSINE,\r\n limit=5)'), ('language', 'lang-py'), ('caption', <wagtail.rich_text.RichText object at 0x3e476cfd2fa0>)])]> To get started, refer to the Firestore Vector Search documentation, Firestore Vector Search extension to generate embeddings documentation and documentation for Firestore’s LangChain and LlamaIndex integrations. Express richer queries Often applications require the ability to express queries that filter on range conditions across multiple fields. For example, if you run an e-commerce site, an end user might want to filter based on a t-shirt size and price ranges. With the recent launch of queries using range filters on multiple fields in preview, you can easily and cost-efficiently perform these queries directly in Firestore. code_block <ListValue: [StructValue([('code', 'db.collection("products")\r\n .whereGreaterThanOrEqualTo("price", 100)\r\n .whereGreaterThanOrEqualTo("rating", 4);'), ('language', 'lang-py'), ('caption', <wagtail.rich_text.RichText object at 0x3e476cfd2640>)])]> To get started, refer to the multiple range queries documentation. We’re also introducing Firestore Query Explain in preview to help you troubleshoot your queries. You can run Explain to retrieve the proposed query plan, or optionally indicate that you’d like to execute the query and analyze the performance, billing and retrieve the actual query results using a special analyze flag: code_block <ListValue: [StructValue([('code', 'Query query = db.collection("products")\r\n .whereGreaterThanOrEqualTo("price", 100)\r\n .whereGreaterThanOrEqualTo("rating", 4);\r\n\r\nExplainResults<QuerySnapshot> explainResults = query.explain(ExplainOptions.builder().analyze(true).build());\r\n\r\nExplainMetrics metrics = explainResults.getMetrics();\r\n\r\nSystem.out.println(metrics.getExecutionStats());'), ('language', 'lang-py'), ('caption', <wagtail.rich_text.RichText object at 0x3e476cfd23d0>)])]> Here’s the output: code_block <ListValue: [StructValue([('code', '{\r\n "executionStats": {\r\n "resultsReturned": "2",\r\n "bytesReturned": "190",\r\n "executionDuration": "0.059943s",\r\n "readOperations": "3",\r\n "debugStats": {\r\n "index_entries_scanned": "500",\r\n "documents_scanned": "2",\r\n "billing_details": {\r\n "index_entries_billable": "500",\r\n "documents_billable": "2",\r\n "small_ops": "0",\r\n "min_query_cost": "0"\r\n }\r\n }\r\n }\r\n}'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x3e476caa80d0>)])]> To get started, refer to the Query Explain documentation. Meeting enterprise needs One key aspect of enterprise readiness is privacy. Firestore now supports Customer Managed Encryption Keys (CMEK) in preview, which allows you to encrypt data stored at-rest using your own specified encryption key. This is an alternative to Firestore’s default behavior, which generates a Google-managed encryption key to encrypt your data. Your customer-specified key can be stored using the Cloud Key Management service, or you can even use your own external key manager. Get started with the Firestore CMEK documentation. And to help minimize serving latency and maximize data privacy, you can now deploy Firestore in any available Google Cloud region. You can review a full list of supported Firestore locations and pricing here. Lastly, you can now retain daily backups using Firestore’s Scheduled Backup feature for up to 98 days, up from seven days. Get started with Firestore Scheduled Backup and Restore today. Next steps To learn more about Firestore and the new features launching at Next ‘24, check out the following resources: Getting Started on Firestore: server client library or web & mobile client library Firestore Vector Search documentation Firestore Vector Search extension to automatically generate embeddings documentation Firestore Gen AI Library Integrations: LangChain and LlamaIndex Firestore Multiple Range queries documentation Firestore Query Explain documentation Firestore Customer Managed Encryption Keys documentation Firestore Locations and Pricing documentation Firestore Scheduled Backup and Restore documentation View the full article

Your product teams might ask - “Why does it take so long to build a feature or application?” Building applications is a heavy lift due to the technical complexity, which includes the complexity of backend services that are used to manage and store data. Every moment focused on this technical complexity is a distraction from delivering on core business value. Firestore alters this by having Google Cloud manage your backend complexity through a complete backend-as-a-service! Firestore is a serverless, NoSQL document database that unlocks application innovation with simplicity, speed and confidence. It acts as a glue that intelligently brings together the complete Google Cloud backend ecosystem, in-app services from Firebase and core UI frameworks & OS from Google. Click to enlarge What is Firestore? Firestore is a serverless, fully managed NoSQL document database that scales from zero to global scale without configuration or downtime. Here’s what makes Firestore unique: Ideal for rapid, flexible and scalable web and mobile development with direct connectivity to the database. Supports effortless real time data synchronization with changes in your database as they happen. Robust support for offline mode, so your users can keep interacting with your app even when the internet isn't available or is unreliable. Fully customizable security and data validation rules to ensure the data is always protected Built-in strong consistency, elastic scaling, high performance & best in class 99.999% availability Integration with Firebase and Google Cloud services like Cloud Functions and BigQuery, serverless data warehouse. In addition to a rich set of Google Cloud service integrations, Firestore also offers deep one-click integrations with a growing set of3rd party partners via Firebase Extensions to help you even more rapidly build applications. Document-model database Firestore is a Document-model database. All of your data is stored in “documents” and then “collections”. You can think of a document as a JSON object. It's a dictionary with a set of key-value mappings, where the values can be several different supported data types including strings, numbers or binary values.. These documents are stored in collections. Documents can't directly contain other documents, but they can point to subcollections that contain other documents, which can point to subcollections, and so on. This structure brings with it a number of advantages. For starters, all queries that you make are shallow, meaning that you can grab a document without worrying about grabbing all the data underneath it. And this means that you can structure your data hierarchically in a way that makes sense to you logically, without having to worry about grabbing tons of unnecessary data. How to use Firestore? Firestore can be used in two modes: Firestore in Native Mode: This mode is differentiated by its ability to directly connect your web & mobile app to Firestore. Native Mode supports up to 10K writes per second, and over a million connections. Firestore in Datastore Mode: This mode supports only server-side usage of Firestore, but supports unlimited scaling, including writes. ConclusionWhatever your application use case may be, if you want to build a feature or an application quickly using Firestore backend-as-a-service. For a more in-depth look into Firestore check out the documentation. For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev. Related Article Cloud Firestore explained: for users who never used Firestore before In this article, I will break down some database basics, terms you should know, what Firestore is, how it works, how it stores data, and ... Read Article