ldbc_snb_datagen_spark
clair
ldbc_snb_datagen_spark | clair | |
---|---|---|
5 | 21 | |
165 | 10,056 | |
1.8% | 0.6% | |
3.7 | 9.2 | |
16 days ago | 8 days ago | |
Java | Go | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ldbc_snb_datagen_spark
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Benchgraph Backstory: The Untapped Potential
Because of the size, complexity, and feedback from the community, we decided to add a larger dataset. So the next dataset should be large, more complex, and recognizable. The choice was easy here; the industry-leading benchmark group Linked Data Benchmark Council (LDBC), which Memgraph is a part of, has open-sourced the datasets for benchmarking. The exact dataset is the social network dataset. It is a synthetically generated dataset representing a social network. It is being used in LDBC audited benchmarks, SNB interactive, and SNB Buissines intelligence benchmarks. Keep in mind that this is NOT an official implementation of an LDBC benchmark, the open-source dataset is being used as a basis for benchmarks, and it will be used for our in-house testing process and improving Memgraph.
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Postgres: The Graph Database You Didn't Know You Had
I designed and maintain several graph benchmarks in the Linked Data Benchmark Council, including workloads aimed for databases [1]. We make no restrictions on implementations, they can any query language like Cypher, SQL, etc.
In our last benchmark aimed at analytical systems [2], we found that SQL queries using WITH RECURSIVE can work for expressing reachability and even weighted shortest path queries. However, formulating an efficient algorithm yields very complex SQL queries [3] and their execution requires a system with a sophisticated optimizer such as Umbra developed at TU Munich [4]. Industry SQL systems are not yet at this level but they may attain that sometime in the future.
Another direction to include graph queries in SQL is the upcoming SQL/PGQ (Property Graph Queries) extension. I'm involved in a project at CWI Amsterdam to incorporate this language into DuckDB [5].
[1] https://ldbcouncil.org/benchmarks/snb/
[2] https://www.vldb.org/pvldb/vol16/p877-szarnyas.pdf
[3] https://github.com/ldbc/ldbc_snb_bi/blob/main/umbra/queries/...
[4] https://umbra-db.com/
[5] https://www.cidrdb.org/cidr2023/slides/p66-wolde-slides.pdf
- Bullshit Graph Database Performance Benchmarks
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From Data Preprocessing to Using Graph Database
Pull the source code from https://github.com/ldbc/ldbc_snb_datagen/tree/stable.To generate data for scale factor 1-1000, use the stable branch.
clair
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I looked through attacks in my access logs. Here's what I found
Besides pointing pentester tools like metasploit at yourself, there are some nice scanners out there.
https://github.com/quay/clair
https://github.com/anchore/grype/
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General Docker Troubleshooting, Best Practices & Where to Go From Here
Clair. Vulnerability Static Analysis for Containers.
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Open source container scanning tool to find vulnerabilities and suggest best practice improvements?
https://github.com/quay/clair 9.4k stars, updated 17 hours ago
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Postgres: The Graph Database You Didn't Know You Had
It scaled well compared to a naive graph abstraction implemented outside the database, but when performance wasn't great, it REALLY wasn't great. We ended up throwing it out in later versions to try and get more consistent performance.
I've since worked on SpiceDB[1] which takes the traditional design approach for graph databases and simply treating Postgres as triple-store and that scales far better. IME, if you need a graph, you probably want to use a database optimized for graph access patterns. Most general-purpose graph databases are just bags of optimizations for common traversals.
[0]: https://github.com/quay/clair
[1]: https://github.com/authzed/spicedb
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Homelab vulnerability/virus scanner
Clair GitHub
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Implement DevSecOps to Secure your CI/CD pipeline
Open source: Trivy, Gryp and Clair are widely used open source tools for container scanning.
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Sublime Music - A FLOSS desktop client for Subsonic API servers (Airsonic, Navidrome, Gonic, etc)
Testing the image with github.com/fullhunt/log4j-scan and https://github.com/quay/clair shows no vulnerabilities
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Automatically tag your Docker images as vulnerable in ECR
Amazon Elastic Container Registry is a fully-managed Docker container registry. It makes it easy for developers to store and manage Docker images inside their AWS environment. ECR supports two types of image scanning. Enhanced image scanning requires an integration with Amazon Inspector. It will scan your repositories continuously. Basic image scanning will use the Common Vulnerabilities and Exposures (CVEs) database (open-source Clair) to find vulnerabilities in your images. You can trigger scans on image push or manually.
- Clair – Vulnerability Static Analysis for Containers
What are some alternatives?
Apache AGE - Graph database optimized for fast analysis and real-time data processing. It is provided as an extension to PostgreSQL.
trivy - Find vulnerabilities, misconfigurations, secrets, SBOM in containers, Kubernetes, code repositories, clouds and more
ldbc_snb_bi - Reference implementations for the LDBC Social Network Benchmark's Business Intelligence (BI) workload
grype - A vulnerability scanner for container images and filesystems
benchgraph
syft - CLI tool and library for generating a Software Bill of Materials from container images and filesystems
arcadedb - ArcadeDB Multi-Model Database, one DBMS that supports SQL, Cypher, Gremlin, HTTP/JSON, MongoDB and Redis. ArcadeDB is a conceptual fork of OrientDB, the first Multi-Model DBMS. ArcadeDB supports Vector Embeddings.
Harbor - An open source trusted cloud native registry project that stores, signs, and scans content.
simple-graph - This is a simple graph database in SQLite, inspired by "SQLite as a document database"
dagda - a tool to perform static analysis of known vulnerabilities, trojans, viruses, malware & other malicious threats in docker images/containers and to monitor the docker daemon and running docker containers for detecting anomalous activities
nebula-docker-compose - Docker compose for Nebula Graph
kubescape - Kubescape is an open-source Kubernetes security platform for your IDE, CI/CD pipelines, and clusters. It includes risk analysis, security, compliance, and misconfiguration scanning, saving Kubernetes users and administrators precious time, effort, and resources.