From dbf3c316ec5bb4b5643f15b87491a96a32fbe2a8 Mon Sep 17 00:00:00 2001 From: Shuyou Date: Wed, 27 Nov 2024 14:46:33 +0800 Subject: [PATCH] translate v2.5.x de --- .../v2.5.x/site/de/about/comparison.json | 2 +- .../v2.5.x/site/de/about/comparison.md | 98 +- .../v2.5.x/site/de/about/limitations.json | 2 +- .../v2.5.x/site/de/about/limitations.md | 100 +- .../v2.5.x/site/de/about/milvus_adopters.json | 2 +- .../v2.5.x/site/de/about/milvus_adopters.md | 20 +- .../v2.5.x/site/de/about/overview.json | 2 +- localization/v2.5.x/site/de/about/overview.md | 175 +- .../v2.5.x/site/de/about/roadmap.json | 2 +- localization/v2.5.x/site/de/about/roadmap.md | 82 +- .../v2.5.x/site/de/adminGuide/allocate.json | 2 +- .../v2.5.x/site/de/adminGuide/allocate.md | 65 +- .../site/de/adminGuide/authenticate.json | 2 +- .../v2.5.x/site/de/adminGuide/authenticate.md | 89 +- .../site/de/adminGuide/chunk_cache.json | 2 +- .../v2.5.x/site/de/adminGuide/chunk_cache.md | 46 +- 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--git a/localization/v2.5.x/site/de/about/comparison.json b/localization/v2.5.x/site/de/about/comparison.json index c4ec151ad..5857f7caa 100644 --- a/localization/v2.5.x/site/de/about/comparison.json +++ b/localization/v2.5.x/site/de/about/comparison.json @@ -1 +1 @@ -{"codeList":[],"headingContent":"Comparing Milvus with Alternatives","anchorList":[{"label":"Comparing Milvus with Alternatives","href":"Comparing-Milvus-with-Alternatives","type":1,"isActive":false},{"label":"Milvus highlights","href":"Milvus-highlights","type":2,"isActive":false},{"label":"Overall comparison","href":"Overall-comparison","type":2,"isActive":false},{"label":"Terminology comparison","href":"Terminology-comparison","type":2,"isActive":false},{"label":"Capability comparison","href":"Capability-comparison","type":2,"isActive":false},{"label":"What's next","href":"Whats-next","type":2,"isActive":false}]} \ No newline at end of file +{"codeList":[],"headingContent":"Comparing Milvus with 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solutions. +title: Vergleich +summary: Dieser Artikel vergleicht Milvus mit anderen Vektorsuchlösungen. --- -

Comparing Milvus with Alternatives

When exploring various vector database options, this comprehensive guide will help you understand Milvus’s unique features, ensuring you choose a database that best fits your specific needs. Notably, Milvus is a leading open-source vector database, and Zilliz Cloud offers a fully-managed Milvus service. To objectively evaluate Milvus against its competitors, consider using benchmark tools to analyze performance metrics.

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Milvus highlights

Bei der Erkundung verschiedener Vektordatenbank-Optionen hilft Ihnen dieser umfassende Leitfaden, die einzigartigen Funktionen von Milvus zu verstehen und sicherzustellen, dass Sie eine Datenbank wählen, die Ihren spezifischen Anforderungen am besten entspricht. Milvus ist eine führende Open-Source-Vektordatenbank, und Zilliz Cloud bietet einen vollständig verwalteten Milvus-Service an. Um Milvus objektiv im Vergleich zu seinen Konkurrenten zu bewerten, sollten Sie Benchmark-Tools zur Analyse der Leistungsmetriken verwenden.

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Milvus-Highlights

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Overall comparison

To compare between Milvus and Pinecone, two vector database solutions, the following table is structured to highlight differences across various features.

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Um Milvus und Pinecone, zwei Vektordatenbanklösungen, miteinander zu vergleichen, ist die folgende Tabelle so strukturiert, dass die Unterschiede zwischen den verschiedenen Merkmalen deutlich werden.

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FeaturePineconeMilvusRemarks
MerkmalPineconeMilvusBemerkungen
Deployment ModesSaaS-onlyMilvus Lite, On-prem Standalone & Cluster, Zilliz Cloud Saas & BYOCMilvus offers greater flexibility in deployment modes.
Supported SDKsPython, JavaScript/TypeScriptPython, Java, NodeJS, Go, Restful API, C#, RustMilvus supports a wider array of programming languages.
Open-source StatusClosedOpen-sourceMilvus is a popular open-source vector database.
ScalabilityScale up/down onlyScale out/in and Scale up/downMilvus features a distributed architecture for enhanced scalability.
AvailabilityPod-based architecture within available zonesAvailable zone failover and cross-region HAMilvus CDC (Change Data Capture) enables primary/standby modes for higher availability.
Perf-Cost (Dollar per million queries)Starts at $0.178 for a medium dataset, $1.222 for a large datasetZilliz Cloud starts at $0.148 for a medium dataset, $0.635 for a large dataset; free version availableRefer to Cost Ranking report.
GPU AccelerationNot supportedSupport NVIDIA GPUGPU acceleration significantly enhances performance, often by orders of magnitude.
BereitstellungsmodiNur SaaSMilvus Lite, On-prem Standalone & Cluster, Zilliz Cloud Saas & BYOCMilvus bietet eine größere Flexibilität bei den Bereitstellungsmodi.
Unterstützte SDKsPython, JavaScript/TypeScriptPython, Java, NodeJS, Go, Restful API, C#, RustMilvus unterstützt eine breitere Palette von Programmiersprachen.
Open-Source-StatusGeschlossenOffene QuelleMilvus ist eine beliebte Open-Source-Vektordatenbank.
SkalierbarkeitNur nach oben/unten skalierenScale out/in und Scale up/downMilvus verfügt über eine verteilte Architektur für verbesserte Skalierbarkeit.
VerfügbarkeitPod-basierte Architektur innerhalb verfügbarer ZonenVerfügbare Zonen-Failover und regionsübergreifende HAMilvus CDC (Change Data Capture) ermöglicht Primär-/Standby-Modi für höhere Verfügbarkeit.
Perf-Kosten (Dollar pro Million Abfragen)Beginnt bei $0,178 für einen mittleren Datensatz, $1,222 für einen großen DatensatzZilliz Cloud beginnt bei $0,148 für einen mittleren Datensatz, $0,635 für einen großen Datensatz; kostenlose Version verfügbarSiehe Bericht Kostenranking.
GPU-BeschleunigungNicht unterstütztUnterstützt NVIDIA GPUDie GPU-Beschleunigung erhöht die Leistung erheblich, oft um Größenordnungen.
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Terminology comparison

Although both serve similar functions as vector databases, the domain-specific terminology between Milvus and Pinecone shows slight variations. A detailed terminology comparison is as follows.

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Obwohl beide ähnliche Funktionen als Vektordatenbanken erfüllen, weist die domänenspezifische Terminologie von Milvus und Pinecone leichte Unterschiede auf. Im Folgenden finden Sie einen detaillierten Terminologievergleich.

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PineconeMilvusRemarks
PineconeMilvusBemerkungen
IndexCollectionIn Pinecone, an index serves as the organizational unit for storing and managing vectors of identical size, and this index is closely integrated with the hardware, known as pods. In contrast, Milvus collections serve a similar purpose but enable handling multiple collections within a single instance.
CollectionBackupIn Pinecone, a collection is essentially a static snapshot of an index, used mainly for backup purposes and cannot be queried. In Milvus, the equivalent feature for creating backups is more transparent and straightforwardly named.
NamespacePartition keyNamespaces allow the partitioning of vectors in an index into subsets. Milvus provides multiple methods like partition or partition key to ensure efficient data isolation within a collection.
MetadataScalar fieldPinecone’s metadata handling relies on key-value pairs, while Milvus allows for complex scalar fields, including standard data types and dynamic JSON fields.
QuerySearchName of the method used to find the nearest neighbors for a given vector, possibly with some additional filters applied on top.
Not availableIteratorPinecone lacks a feature for iterating through all vectors in an index. Milvus introduces Search Iterator and Query Iterator methods, enhancing data retrieval capabilities across datasets.
IndexSammlungIn Pinecone dient ein Index als Organisationseinheit für die Speicherung und Verwaltung von Vektoren gleicher Größe, und dieser Index ist eng mit der Hardware, den so genannten Pods, verbunden. Im Gegensatz dazu dienen Milvus-Sammlungen einem ähnlichen Zweck, ermöglichen aber die Verwaltung mehrerer Sammlungen innerhalb einer einzigen Instanz.
SammlungSicherungIn Pinecone ist eine Sammlung im Wesentlichen ein statischer Schnappschuss eines Indexes, der hauptsächlich für Sicherungszwecke verwendet wird und nicht abgefragt werden kann. In Milvus ist die entsprechende Funktion zur Erstellung von Backups transparenter und einfach benannt.
NamensraumPartitionsschlüsselNamespaces ermöglichen die Partitionierung von Vektoren in einem Index in Teilmengen. Milvus bietet mehrere Methoden wie Partition oder Partitionsschlüssel, um eine effiziente Datenisolierung innerhalb einer Sammlung zu gewährleisten.
MetadatenSkalares FeldDie Handhabung von Metadaten in Pinecone basiert auf Schlüssel-Wert-Paaren, während Milvus komplexe skalare Felder, einschließlich Standard-Datentypen und dynamische JSON-Felder, zulässt.
AbfrageSucheName der Methode, die verwendet wird, um die nächsten Nachbarn für einen gegebenen Vektor zu finden, möglicherweise mit einigen zusätzlichen Filtern, die darüber hinaus angewendet werden.
Nicht verfügbarIteratorIn Pinecone fehlt eine Funktion zur Iteration durch alle Vektoren in einem Index. Milvus führt die Methoden Search Iterator und Query Iterator ein, die die Möglichkeiten der Datenabfrage in verschiedenen Datensätzen verbessern.
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Capability comparison

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CapabilityPineconeMilvus
FähigkeitPineconeMilvus
Deployment ModesSaaS-onlyMilvus Lite, On-prem Standalone & Cluster, Zilliz Cloud Saas & BYOC
Embedding FunctionsNot availableSupport with pymilvus[model]
Data TypesString, Number, Bool, List of StringString, VarChar, Number (Int, Float, Double), Bool, Array, JSON, Float Vector, Binary Vector, BFloat16, Float16, Sparse Vector
Metric and Index TypesCos, Dot, Euclidean
P-family, S-family		Cosine, IP (Dot), L2 (Euclidean), Hamming, Jaccard
FLAT, IVF_FLAT, IVF_SQ8, IVF_PQ, HNSW, SCANN, GPU Indexes
Schema DesignFlexible modeFlexible mode, Strict mode
Multiple Vector FieldsN/AMulti-vector and hybrid search
ToolsDatasets, text utilities, spark connectorAttu, Birdwatcher, Backup, CLI, CDC, Spark and Kafka connectors
BereitstellungsmodiNur SaaSMilvus Lite, On-prem Standalone & Cluster, Zilliz Cloud Saas & BYOC
Funktionen einbettenNicht verfügbarUnterstützung mit pymilvus[model]
DatentypenString, Zahl, Bool, Liste von StringString, VarChar, Number (Int, Float, Double), Bool, Array, JSON, Float Vector, Binary Vector, BFloat16, Float16, Sparse Vector
Metrische und Index-TypenCos, Dot, Euklidisch
P-Familie, S-Familie		Cosinus, IP (Punkt), L2 (euklidisch), Hamming, Jaccard
FLAT, IVF_FLAT, IVF_SQ8, IVF_PQ, HNSW, SCANN, GPU Indizes
Schema-EntwurfFlexibler ModusFlexibler Modus, Strenger Modus
Mehrere VektorfelderK.A.Multivektor- und Hybridsuche
WerkzeugeDatensätze, Text-Utilities, Spark-KonnektorAttu, Birdwatcher, Backup, CLI, CDC, Spark- und Kafka-Konnektoren
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Key insights