Glossary
- Hierarchical Navigable Small Worlds graphs
- Algorithm for performing efficient nearest neighbor search in multi-dimensional space. Atlas Vector Search performs ANN search with Hierarchical Navigable Small Worlds.
- alert
Notification sent by Atlas when your database operations or server usage reach thresholds that affect cluster performance. To learn what conditions you can set to trigger alerts, see Review Alert Conditions.
- analytics node
- Specialized read-only node that can isolate queries which you do not want to affect your operational workload. Analytics nodes are useful for handling analytic data such as reporting queries executed by BI tools. You can host analytics nodes in dedicated geographic regions to optimize read performance and reduce latency.
- API
Communication protocol facilitating interaction between the client and MongoDB Atlas. You can use the Atlas Administration API to automate many of the tasks performed in the Atlas UI.
- Approximate Nearest Neighbor (ANN) search
- Computational technique used to quickly find points in a dataset that are close to a given query point. Atlas Vector Search uses ANN search to find vector embeddings in the data that are closest to the vector embeddings in the Atlas Vector Search query without scanning every vector.
- Atlas Search
Fine-grained text indexing enabling advanced text search on your data without any additional required management. Atlas Search provides options for several kinds of text analyzers, score-based results ranking, and a rich query language.
- Atlas user
Account used to access the Atlas application. You can grant Atlas users access to Atlas organizations, projects, or both, with certain permissions defined by user roles. Atlas users are different than database users. Atlas users do not provide access to any MongoDB databases.
- Atlas user role
Set of permissions granted to an Atlas user. You can grant permissions at the organization or project level.
- Atlas Vector Search
- Feature in Atlas that allows you to perform semantic search on vector embeddings by comparing query vectors with indexed vectors to find the closest match.
- auto-scaling
Configurable option to have your cluster automatically increase or decrease its cluster tier, storage capacity, or both in response to cluster usage.
- backup
Copy of your data that encapsulates the state of your cluster at a given time. Backups provide a safety measure in the case of data loss events.
Atlas provides fully-managed Cloud Backups.
- cloud backups
Localized cluster backup storage using the native snapshot functionality of the cluster's cloud service provider.
Atlas supports Cloud Backups for clusters served on:
- cluster
- Set of nodes comprising a MongoDB deployment. In Atlas, clusters can be replica sets or sharded deployments.
- cluster class
Configurable for M40+ clusters hosted on AWS.
Storage class of your cluster. Your selected class affects cluster storage performance and cluster costs. You can choose one of the following classes:
Low CPU
General
Local NVMe SSD
- cluster tier
Dictates the memory, storage, vCPUs, and IOPS specification for each data-bearing server in the cluster. Cluster storage size and overall performance increase as the cluster tier increases.
- cosine similarity
- Metric that uses the angle between two vectors to determine the similarity between those vectors. Cosine similarity is sensitive to vector orientation. You can use cosine similarity function when indexing your vector embeddings for Atlas Vector Search. If the vectors are normalized to unit length, use dotProduct similarity function instead.
- custom role
Custom set of MongoDB privilege actions and MongoDB roles that you can save and assign to database users. Create custom roles when Atlas's built-in roles don't describe your desired set of privileges.
- Data Explorer
Tool within Atlas to view and interact with cluster data. You can also use the Data Explorer to manage indexes and run aggregation pipelines to process your data.
- Data Federation
MongoDB's solution for querying data stored in low-cost S3 buckets, Atlas clusters, and HTTP endpoints using the MongoDB Query Language. Analytics applications can use Atlas Data Federation to make use of archived data for their data processing needs.
- data ingestion pipeline
- Workflow for organizing and transforming data by using RAG and storing it in a vector database such as Atlas.
- Database user
Credentials used to authenticate a client to access a MongoDB cluster. You can assign privileges to a database user to determine that user's access level to a cluster. Database users are different from Atlas users. Database users have access to MongoDB deployments, not the Atlas application.
- Database user privileges
Set of privilege actions or roles granted to a database user. You can assign database user privileges at the cluster, database, and collection level.
- dead letter queue
- A dead letter queue is a collection within an Atlas database that stores documents that throw errors during ingestion.
- dedicated cluster
Cluster category containing clusters of tier
M10and greater.TierRecommended environmentsM10andM20Development
Low-traffic production
M30and greaterProduction
- dense vectors
- Numeric representation of data where most or all of the dimensions contain non-zero values. Atlas Vector Search uses dense vectors, which are packed with more data, to capture more complex relationships.
- deployment
- A group of MongoDB servers containing your data. Atlas-managed clusters are clusters (replica sets or sharded clusters).
- dimensions
- Number of components or elements that make up the features or
attributes of data in multi-dimensional space. Atlas Vector Search supports up
to
4096dimensions at index-time and query-time. - dotProduct similarity
- Measures similarity between two vectors in multi-dimensional
space and returns a scalar value, which is positive when the
vectors point in roughly the same direction, negative when the
vectors point in opposite direction, and zero when the vectors
have no similarity. Atlas Vector Search supports using
dotproductsimilarity function when searching for nearest neighbors. We recommend this similarity function instead of cosine similarity if the vectors are normalized to unit length. - electable node
- Node which is eligible to become the primary member of your replica set. Atlas prioritizes nodes in the Highest Priority region for primary eligibility during elections. To ensure reliable elections, the total number of electable nodes across an entire region must be 3, 5, or 7.
- embedding
- Representation of data such as text, images, audio, video,
and so on as an array of numbers, which can be interpreted as
coordinates in multi-dimensional space. Atlas supports storing
embeddings in an Atlas cluster and Atlas Vector Search supports indexing
and querying vector embeddings of up to
4096dimensions. - encryption key
Random string of bits generated specifically to encrypt and decrypt data.
Atlas
Project Ownerscan configure an additional layer of encryption on their data in addition to the default encryption at rest that Atlas provides. Project owners can use their Atlas-compatible customer key management provider with the MongoDB encrypted storage engine.Atlas supports the following customer key management providers when configuring Encryption at Rest:
- euclidean similarity
- Formula to calculate the similarity by using the distance between
two vectors in multi-dimensional space. Euclidean distance is
sensitive to the magnitude of the vectors. Atlas Vector Search supports using
euclideansimilarity function for indexing vectors and when searching for nearest neighbors. - Free Tier
Free-to-use cluster tier that provides a small-scale development environment to host your data. Free clusters never expire, and provide access to a subset of Atlas features and functionality. Free clusters might also be referred to by their instance size,
M0.- Global Cluster
Clusters with defined geographic zones to support location-aware read and write operations for globally distributed application instances and clients. You can enable global sharding on clusters of tier
M30and greater.- global write zone
Geographic zone representing a subset of your global cluster distribution. Each Global Cluster supports up to 9 distinct global write zones. Each zone consists of one Highest Priority region and one or more electable, read-only, or analytics regions.
The available geographic regions depend on the selected cloud service provider.
- group
- See project.
- group ID
- See project ID.
- Highest Priority region
Region in a multi-region cluster which Atlas prioritizes for primary eligibility during elections.
- hybrid search
- Method of combining different search methods, such as a full-text and a semantic search, to take advantage of their respective strengths. The results are combined by using a technique such as Reciprocal Rank Fusion (RRF).
- Impact
Estimated performance improvement of an index that Performance Advisor suggests.
- interface endpoint
AWS VPC endpoint with a private IP address that sends traffic to the Atlas private endpoint service over AWS PrivateLink.
- IP access list
List of IP addresses and CIDR blocks with access to clusters within an Atlas project. For client connections over the public Internet, Atlas allows connections to a cluster only from entries in the corresponding project's IP access list. The access list may have up to 200 entries.
Atlas also allows client connections over nonpublic networking, such network peering connections or private endpoints. These types of connections work irrespective of the IP access list. To learn more, see Set Up a Network Peering Connection and Learn About Private Endpoints in Atlas.
- K-nearest neighbor search
- Given a set of points P with a defined similarity function S, for a query point q, finds the set of k points in P with the best values of S*(*p, q). Atlas Vector Search ENN search returns the exact top k points and ANN search returns k points that are similar to q, but not necessarily the k most similar to q.
- LDAP
Cross-platform protocol used to authenticate users and authorize them to access data on a cluster. You can use Atlas to manage user authentication and authorization from all MongoDB clients using your own LDAP server over TLS. A single LDAPS configuration applies to all clusters in an Atlas project.
- link-token
String that contains the information necessary to connect from Cloud Manager or Ops Manager to Atlas during a live migration from a Cloud Manager or Ops Manager deployment to a cluster in Atlas.
When you are ready to live migrate data from a Cloud Manager or Ops Manager deployment, you generate a link-token in Atlas and then enter it in your Cloud Manager or Ops Manager organization's settings. You use the same link-token to migrate each deployment in your Cloud Manager or Ops Manager organization sequentially, one at a time. You can generate multiple link-tokens in Atlas. Use one unique link-token for each Cloud Manager or Ops Manager organization.
- Live Migration
Process to seamlessly move an existing source replica set or sharded cluster to Atlas. During the live migration process, Atlas keeps the target cluster in sync with the remote source until you cut your applications over to the Atlas cluster. Atlas offers two modes of live migration:
Push live migration, known in the Atlas user interface as Live Migration from Ops Manager or Cloud Manager, where Atlas pushes a deployment from Cloud Manager or Ops Manager to Atlas.
Pull live migration, known in the Atlas user interface as General Live Migration, where Atlas pulls a deployment from a cloud or on-premise deployment to Atlas.
- maintenance window
Day and time of the week when Atlas should start weekly maintenance on your cluster. You can set your maintenance window in your Project Settings.
Important
Maintenance Window Considerations
- Urgent Maintenance Activities
- Urgent maintenance activities such as security patches cannot wait for your chosen window. Atlas will start those maintenance activities when needed.
- Ongoing Maintenance Operations
- Once maintenance is scheduled for your cluster, you cannot change your maintenance window until the current maintenance efforts have completed.
- Maintenance Requires Replica Set Elections
- Atlas performs maintenance the same way as the maintenance procedure described in the MongoDB Manual. This procedure requires at least one replica set election during the maintenance window per replica set.
- Maintenance Starts As Close to the Hour As Possible
- Maintenance always begins as close to the scheduled hour as possible, but in-progress cluster updates or unexpected system issues could delay the start time.
- MongoDB Charts
Visualization tool for your Atlas data. You can launch MongoDB Charts from your Atlas cluster and view your data with the Charts application to begin visualizing your data.
- multi-region cluster
Atlas cluster spanning multiple geographic regions. Multi-region clusters can increase availability and improve performance by routing application queries to the most appropriate geographic regions.
Multi-region clusters must contain electable nodes.
Multi-region clusters may contain read-only nodes and analytics nodes.
- namespace
- Combination of the database name and the collection name in the
database.collectionformat. - Namespace Insights
Atlas tool that monitors collection-level query latency. You can view query latency metrics and statistics for certain hosts and operation types. Manage pinned namespaces and choose up to five namespaces to show in the corresponding query latency charts.
- network peering connection
Process by which two Internet networks connect and exchange traffic. You can directly peer your VPC with the Atlas VPC created for your MongoDB clusters. Using network peering, your application servers can directly connect to Atlas while remaining isolated from public networks.
- NVMe storage
Available for M40+ clusters hosted on AWS
For applications hosted on AWS which require low-latency and high-throughput IO, you can use the NVMe cluster class. The NVMe cluster class leverages a unique data protocol to greatly improve data access speeds.
NVMe clusters use a hidden secondary node consisting of a provisioned volume with high throughput and IOPS to facilitate backup.
- operational node
- Any electable node or a read-only node in your Atlas cluster.
- organization
Logical grouping of Atlas projects. You can leverage an organization to manage billing, users, and security settings for the projects it contains.
Billing happens at the organization level while preserving visibility into usage in each project.
You can view all projects within an organization.
You can use teams to bulk assign organization users to projects within the organization.
- organization ID
- Unique 24-digit hexadecimal string used to identify your Atlas organization. The Return All Organizations endpoint returns the ID of all organizations that the authenticated user executing the API call can access.
- Performance Advisor
Atlas tool that monitors slow queries executed on your cluster and suggests indexes to improve query performance. Each index that the Performance Advisor suggests include an Impact score indicating the potential performance improvement that index would bring.
- primary
- In a replica set, the primary is the member that receives all write operations. In multi-region clusters, Atlas prioritizes nodes in the Highest Priority region for primary eligibility during elections.
- project
Logical grouping of clusters. You can have multiple clusters within a single project and multiple projects within a single organization.
Note
Project is synonymous with group.
- project ID
Unique 24-digit hexadecimal string used to identify your Atlas project. The Get All Projects API endpoint returns the ID of all projects that the authenticated user executing the API call can access.
Note
Project ID is synonymous with group ID.
- quantization
- Quantization is the process of compressing the value of dimensions to a smaller range to allow the storage of more vectors or of vectors with higher dimensions. Atlas Vector Search supports automatic quantization of full-fidelity vectors to reduce memory and storage costs, and ingestion and indexing of scalar and binary quantized vectors from embedding models.
- Query Profiler
Atlas tool that diagnoses and monitors performance issues in your cluster. The Query Profiler can expose long-running queries and their performance statistics. You can filter the data returned by the Query Profiler to hone in on specific namespaces and operation types.
- read-only node
- Replica set in a dedicated geographic region that supplements your electable node regions. You can use read-only nodes to localize data where it is most frequently read to improve performance.
- Real-Time Performance Panel
Atlas monitoring service that displays current network traffic, database operations on your clusters, and hardware statistics about your host machines. Use the RTPP to visually evaluate query execution times, monitor network activity, and discover potential replication lag on secondary members of replica sets.
- recall
- Measures the fraction of true nearest neighbors that were returned by an ANN search. This measure reflects how close the algorithm approximates the results of ENN search. The notation Recall@k refers to the measurement of how many of the true nearest neighbors were present in the top k results returned by Atlas Vector Search.
- replica set
Group of MongoDB servers that maintain the same data set. Replica sets provide redundancy, high availability, and are the basis for all production deployments.
- rolling restart
- Process that restarts all nodes in the cluster in sequence. To maintain cluster availability, Atlas restarts one node at a time starting with a secondary node. Atlas always maintains a primary node until the rolling restart completes.
- scalar quantization
- Scalar quantization involves selecting the minimum and maximum values across all indexed vectors within a segment for each dimension, and producing equally sized bins between them. The mappings for each of these dimensions to the bins yields the new quantized values. Atlas Vector Search supports automatic scalar quantization for your float32 vectors, and ingestion and indexing of your scalar quantized vectors from embedding providers.
- semantic search
- Search for values that have a similar meaning to query. Semantic search captures the natural relationship between words or phrases even when there is no lexical overlap. Semantic search and vector search are often used interchangeably. Atlas Vector Search supports semantic search on vector data stored in Atlas clusters.
- sharded cluster
Set of nodes comprising a sharded MongoDB deployment. A sharded cluster consists of config servers, shards, and one or more
mongosrouting processes.- shared cluster
Cluster category containing
M0(Free Tier),M2, andM5tier clusters. Shared clusters are generally used for development and small production workloads.- similarity function
- Measures the similarity between two vectors. Atlas Vector Search supports
euclidean,cosine, anddotProductsimilarity functions. - snapshot
Backup of your data captured at a specific interval and stored in a backup data center. The Snapshot Schedule determines the interval for taking snapshots and how long to store them.
- team
Group of Atlas users in the same organization. You can use teams to grant access to the same group of Atlas users across multiple projects. All users in the team share the same project access.
Note
Atlas users can belong to multiple teams.
- topology
State of a deployment of MongoDB instances, which includes the following details:
- vector database
- System that stores vector embeddings and associated metadata, and enables nearest neighbor search on the stored vector embeddings. You can use Atlas as your vector database and Atlas Vector Search to perform vector search on the stored vector embeddings. You can use vector database to implement RAG.
- vector index
- Data structure that efficiently processes nearest neighbor search
queries. Atlas Vector Search supports creating indexes of type
vectorto index fields for running$vectorSearchqueries. - vector search
- Method of performing k nearest neighbor search over a set of vectors stored in a vector index. Atlas Vector Search supports ANN and ENN search for k nearest neighbors.