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ArangoDB 3.9, released on February 15, 2022, introduced a way for Enterprise Edition deployments to improve data locality for selected graph traversals: SmartGraphs can use SatelliteCollections. Because satellite data is replicated to participating DB-Servers, queries that use it can do more work locally and reduce network hops. That is a workload-dependent architectural benefit—not a guaranteed speedup, and the release materials do not give a universal performance figure.
What changed for graph workloads in ArangoDB 3.9?
The key graph-scaling change was the ability to include SatelliteCollections in SmartGraph definitions. In a clustered deployment, a SatelliteCollection is replicated to participating DB-Servers. When a traversal uses data in that collection, parts of the query can run locally on those servers rather than needing to fetch the same data across the cluster.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsArangoDB’s version-specific documentation describes this as a way to improve data locality and reduce network hops. This matters because distributed graph queries can involve repeated movement of vertex and edge data between servers. Keeping suitable data available where traversal work happens can reduce that coordination. It does not eliminate all network traffic, nor does the documentation promise that every query will become faster.
ArangoDB announced 3.9 general availability on February 15, 2022. Its release announcement also highlighted Hybrid SmartGraphs, ArangoSearch analyzer additions, and AQL changes. The SatelliteCollection capability is the most directly relevant of those changes for graph operations at cluster scale.
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How do SmartGraphs and SatelliteCollections work together?
SmartGraphs organize graph data across a cluster using a graph’s sharding strategy. SatelliteCollections provide replicated data that is available on participating DB-Servers. Combining them allows certain traversal work to use locally available satellite data, reducing the need for remote reads in appropriate workloads.
The release documentation says regular traversals, weighted traversals, shortest-path queries, and k-shortest-path queries can partially execute locally on each DB-Server when SatelliteCollection data is involved. “Partially” is important: a query may still require coordination or remote work depending on its shape, the graph’s distribution, and which data it touches.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11ArangoDB also documents an optimization for disjoint SmartGraphs. A graph is disjoint when edges do not connect vertices with different SmartGraph attribute values. In many cases, the optimizer can push execution to a single DB-Server for a disjoint graph. This is a separate topology-related opportunity for locality, not a property to enable if the application needs edges across those components.
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When is this approach a good fit?
SatelliteCollections are most appropriate when the replicated collection is small enough to copy to each participating DB-Server and appears frequently in traversals. The goal is to trade replication and storage for better locality during relevant queries.
- Consider it when a relatively small collection is repeatedly needed by graph traversals and keeping a copy on each participating server is practical.
- Check the graph topology before using the disjoint option. If edges must connect vertices across the graph’s disjoint components, that option does not match the data model.
- Measure the workload before assuming a benefit. Compare the actual traversal latency and resource use, and inspect how much network communication the query requires.
Replication is the trade-off: each participating DB-Server holds a copy of the satellite data. A collection that is too large or changes in ways that make replication costly may be a poor fit. The documentation establishes the locality mechanism, but does not supply a general benchmark or percentage speedup. Any numerical claim should come from a test of the specific deployment and queries.
Which ArangoDB 3.9 changes are separate from graph locality?
Several other 3.9 additions can help analytics or query behavior, but they should not be confused with SmartGraph data placement.
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- ArangoSearch analyzers: the new
segmentationAnalyzer supports language-agnostic tokenization for mixed-language strings. ThecollationAnalyzer follows language-specific comparison rules, which can be useful for range queries. - AQL scoring and similarity functions: exponential, linear, and Gaussian decay functions were added, along with cosine similarity, Manhattan distance (
L1_DISTANCE), and Euclidean distance (L2_DISTANCE) functions. - Traversal filtering: post-filters for vertices or edges can be applied during traversal to avoid producing output for non-matching paths. This can reduce unnecessary result generation, but it is distinct from placing data locally.
How can operators diagnose cluster query behavior?
ArangoDB 3.9 adds useful observability signals, although they do not make queries faster by themselves. In cluster deployments, query profiling includes the number of HTTP requests. The query explainer also distinguishes index scans that still require document lookups, helping operators see when an apparent index-based plan has additional retrieval work.
Starting with 3.9.0, responses include the x-arango-queue-time-seconds header. A client can use this value to detect time spent waiting in the server-side queue and respond to overload. Clients can also provide a maximum accepted queue time; requests exceeding that limit may be rejected with HTTP 412. This is a control and diagnostic signal, not an optimization for traversal execution.
For a meaningful locality evaluation, examine the query plan and profiling data, then compare latency and resource use for the same representative workload and deployment conditions. Look at data placement, whether edges cross graph components, the query’s network hops, and the edition in use. The available release documentation does not establish comparative benchmark results for these configurations.
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What should teams know before upgrading to 3.9?
The SatelliteCollection-and-SmartGraph combination was limited to Enterprise Edition in 3.9. Teams should also distinguish release-era feature support from the status of later product versions; the claims here concern the 3.9 documentation and do not establish current compatibility or support arrangements.
There was a specific cluster-upgrade issue in versions 3.9.0 through 3.9.2: when a collection in the 3.8 cluster had a schema, background maintenance could enter a busy loop. ArangoDB’s known-issues record lists the problem as fixed in 3.9.3. This caveat applies to those patch releases, not to every 3.9 version.
In short, 3.9’s graph-scaling story is about moving suitable data closer to traversal execution. Whether that reduces latency or resource use depends on the collection size, graph topology, query pattern, and cluster configuration.
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Frequently Asked Questions
Is ArangoDB 3.9’s SatelliteCollection feature available in Community Edition?
No. The 3.9 documentation limits SmartGraphs using SatelliteCollections to Enterprise Edition. Confirm the edition and version requirements that apply to the deployment you plan to run.
Does a disjoint SmartGraph allow edges between its components?
No. In this context, disjoint means edges do not connect vertices with different SmartGraph attribute values. If cross-component edges are part of the application’s graph, the disjoint optimization is not suitable.
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It reports server-side queueing time for the request, giving a client a signal that the server may be overloaded. A client can use a maximum accepted queue time to reject requests that wait too long; the documented response for exceeding that limit is HTTP 412.
Did ArangoDB publish a speedup percentage for SmartGraphs with SatelliteCollections in 3.9?
The version-specific documentation describes local execution and fewer network hops but does not provide a general quantified speedup. Results therefore need to be established against the queries and cluster configuration that matter to a particular deployment.
