Release notes for all patch versions of GraphDB |version|, including an overview of new features, known and resolved bugs, and changes in connector versions.

This patch release include mostly changes in query engine, and a security fix around permission for function manipulation, as well as the usual dependency updates Changes Core Removed automatic us...

Query, model, visualise and analyse graph databases with ease. The ultimate data studio for Amazon Neptune, Spanner Graph, Neo4j, GraphDB and 30+ others.

日立ソリューションズは、プラントなどの設備保全業務向けに、定期修繕の進捗管理や工事情報の可視化、GraphDBと生成AIを用いたナレッジ活用アプリ、上空と地上の画像を管理するプラットフォームど、現場業務の効率化と技能継承を実現する最新の保全DXソリューションを提案している。

Patch release with mostly fixes in query engine and dependencies updates Changes Core Fix using of @this in condition comparing with record ids successfully, issue #10835 Fix usage of in syntax wi...

Let's delve into GraphRAG, learning about KùzuDB, node embeddings vs text embeddings, and LangChain, running on top of Ollama with phi4 and gemma3.

Posted by disclosure via Fulldisclosure on Aug 170day Rubbish Research Team is publicly disclosing a vulnerability in Ontotext GraphDB 11.4.3 Free edition (Ontotext / Graphwise). The research is published and a proof-of-concept is available. Pre-authentication RCE (CVSS 9.8, pre-authentication) Ontotext GraphDB 11.4.3 Free edition defaults to security=false, which bypasses the entire Spring Security filter chain. As a result, /rest/repositories/ruleset/upload and /repositories/* are...

_A field guide for anyone choosing a managed graph database on a budget - and a template for not trusting benchmarks, including this one._ Full repo, all the code, and the raw results: **https://github.com/HassanYosuf/graphdb-benchmark** Every graph database vendor's homepage says roughly the same thing: fast traversals, no JOINs, point your driver at a URL and go. That pitch is almost never wrong, and almost never the whole story either. The part that actually decides whether your side project or your MVP survives contact with real traffic is what happens on the smallest, cheapest instance you can get — because that's the one you're actually going to run. So instead of writing another "top 10 graph databases" listicle, I built a small graph, wrote nine queries that mirror what a real app does with a graph (look someone up, walk their network two hops, recommend something, find a path, add data), and ran the exact same queries against five databases sitting on the smallest tier each one offers: **CognoDB Cloud** , **Memgraph** , **Neo4j Community** , **ArangoDB Oasis** , and **FalkorDB Cloud**. Neo4j AuraDB Free was going to be a sixth, single-most-direct comparator, but its signup flow asked for billing details before it would finish provisioning an instance — which broke this benchmark's own "free tier, no credit card" rule, so it's left out of the results rather than run on different terms than everything else here. The code supports it end-to-end if you have (or are willing to set up) an Aura instance that doesn't hit that wall. ## Why "same resources" is the whole ballgame The easiest way to win a database benchmark is to accidentally (or not) compare a free tier against a bigger box. A burstable 0.5 vCPU / 256 MB instance and a dedicated 4 vCPU / 16 GB instance are not the same product, even if they run the same software — and yet "Database X is 8x faster than Database Y" headlines routinely come from exactly that kind of mismatch. So the first rule here was: **every database runs on its smallest tier, every tier's specs are written down, and the dataset is sized to fit the tightest one of the five.** Where a vendor doesn't let you dial in an exact vCPU/RAM number for their free tier (looking at you, most managed clouds), the post says so instead of guessing. ## What was actually measured Five databases, one dataset (7,000 people, 2,800 products, ~56,000 relationships — big enough to have real hub nodes and skewed degree distributions, small enough to fit inside FalkorDB's real 100 MB free-tier cap with room to spare, which turned out to be the tightest constraint in the whole comparison, not CognoDB's), nine queries run identically everywhere: * a point lookup * a 1-hop and a 2-hop traversal ("friends of friends" — the query graph databases exist to make fast) * a two-edge-type recommendation pattern * a bounded shortest path * two aggregations (one scoped to a user, one global) * two writes (create a node, create an edge) For each one: p50/p95/p99 latency and throughput, plus load time and connection setup time as their own separate numbers — "how long until my data is queryable" and "how fast is each query once it's loaded" are different questions with different answers. ## What was found The self-hosted engines won on raw query speed, and that's not a surprise once you remember what "free tier" is buying on each side. Memgraph and Neo4j Community — both running under a 0.5 vCPU / 256 MB Docker cap, but on the same machine as the client — answered point lookups and 1-hop reads in **1-4ms**. (That 256 MB cap was set to match the assignment brief's stated CognoDB spec; the real CognoDB dashboard reports 512 MB, so if anything the self-hosted numbers here are a _more_ resource-starved baseline than CognoDB itself, not an equal one — disclosed rather than quietly fixed, since fixing it means re-running everything.) CognoDB, ArangoDB Oasis, and FalkorDB Cloud all sat in a narrow band regardless of query shape: roughly **245-310ms** for CognoDB and Oasis, **~35ms** for FalkorDB, on everything from a point lookup to a two-hop traversal. That flatness is the tell — for those three, network round-trip to the provider's region is doing most of the work in the number, not the query planner. If you're picking a database to make _queries_ fast, self-hosting next to your app wins by construction; if you're picking a _managed_ free tier, FalkorDB's ~35ms floor was the best of the three managed options that completed reliably. Two results don't fit the "just network latency" story and are worth flagging on their own: 1. **CognoDB's free instance didn't hold up under sustained load** on two of the nine workloads: `shortest_path_two_people` completed only 1 of 100 iterations and `aggregation_spend_by_category` completed 24 of 100, both failing with the connection dying mid-query (`SSLEOFError` / `ConnectionResetError`). The identical Cypher, on the identical driver, ran error-free on every other target including self-hosted Neo4j Community — so this reads as the free instance itself under load, not a query bug. 2. **Memgraph — otherwise the fastest thing in the whole comparison — fell off a cliff on exactly the two multi-hop queries** : `two_hop_friends_of_friends` at p50 **12.1s** and `recommendation_bought_similar` at p50 **2.3s** , against sub-4ms for its own point lookups and writes. The dataset's edges are generated with a Pareto/Zipf skew to produce hub nodes on purpose, and these are the two workloads that fan out through them — a plausible explanation, though this wasn't isolated further within the time budget. Load time told a different story than query latency, and it's easy to miss if you only look at p50s. FalkorDB had the snappiest per-query latency of the managed clouds but the slowest bulk load by far (**231s** , vs. CognoDB's 40s and Neo4j Community's 21s) — "fast once loaded" and "fast to get data in" are genuinely different axes, and a workload that reloads data often should weight the second one more than the headline query numbers suggest. ## A few things worth keeping in mind, regardless of which numbers land where **Network latency isn't zero, and this doesn't pretend it is.** Every managed provider here runs in a region you pick when you sign up, and the client-observed latency includes real round-trip time to that region. If you're picking a database for an app with users in a specific place, the closest region beats the fastest engine more often than people expect. **"Free tier" doesn't mean "same free tier."** Some platforms publish exact vCPU/RAM numbers for their smallest tier; some don't and just say "shared" or "burstable." The repo writes down what each one actually tells you, rather than assuming parity where none is documented. **No single number wins.** There's no composite score across nine different query shapes here, on purpose. A database that's excellent at 2-hop traversal and mediocre at global aggregation isn't "worse" — it's a better fit for a social app than an analytics dashboard. The right benchmark for _your_ database choice is the one built from _your_ queries, not this one. The repo is structured so you can swap in your own dataset and workloads in an afternoon. ## Why CognoDB in particular CognoDB's whole pitch is compatibility: point the official Neo4j driver at a `bolt+s://` URL with a username and password, and nothing else about your code changes. That's a testable claim, not a marketing one — so it got tested, using the same connector class, the same driver, and the same query strings for CognoDB and self-hosted Neo4j Community. The same class also supports Neo4j AuraDB with no code changes, for the same reason — it just isn't in this run's results (see the billing-details note above). If a query behaves differently on CognoDB than on Neo4j, that's a difference in the database, not in how it was talked to. ## Run it yourself This isn't a report you have to trust — it's a repo you can run. Clone it, drop in your own free-tier credentials, and get your own numbers on your own network in about twenty minutes: git clone https://github.com/HassanYosuf/graphdb-benchmark cd graphdb-benchmark ./scripts/setup_all.sh cp .env.example .env # fill in your credentials docker compose up -d memgraph neo4j-community ./scripts/run_all.sh Whatever you find, I'd rather you find it yourself than take my word for it. That's the point. _Repo: https://github.com/HassanYosuf/graphdb-benchmark — methodology, connectors, raw JSON results, and charts are all in there._

Staff Software Engineer at Atomic Financial - Node.js, TypeScript, JavaScript, AWS, ECS, EKS, Lambda, DynamoDB, Aurora, SQS, SNS, IAM, GitHub Actions, Terraform, CloudFormation, MongoDB, GraphDB, Temp...

Logistics optimization is becoming more complex: multi-hop routing, diverse transportation modes, SKU-level requirements, service constraints, and constantly shifting supply-demand conditions — leavin...