RedisGraph 2.0 by Pieter Cailliau - Redis Day Bangalore 2020

Jan 29, 2020Download as PPTX, PDF0 likes89 views

RedisGraph 2.0 provides significant improvements including: - Full text search support through embedded RediSearch 1.6 enabling graph-aided search. - Support for returning full graph responses to enable better visualization. - Broad support for Cypher including triadic closure and new graph-aided search capabilities. - Performance improvements of up to 3.7x faster operations per second and 3.9x faster query times compared to RedisGraph v1.2. - Support for benchmarking including the LDBC benchmark.

RedisGraph 2.0
Pieter Cailliau
Product Manager, Redis Labs

PRESENTED
BY
What is RedisGraph?
Adjacency Matrices

PRESENTED
BY
● Full Text Search (FTS) support
○ Embedded RediSearch 1.6
○ Enables Graph-aided Search.
● Full graph response
○ Enabling visualisation.
○ RedisInsight via Ogma.js ( linkurio.us/ )
○ Third party integrations announced soon
● Significant amount of Cypher additions.
RedisGraph 2.0
Headlines

PRESENTED
BY
What’s new?
Broad support for Cypher

PRESENTED
BY
What’s new?
Broad support for Cypher - Triadic Closure
B
A
C
FRIENDS FRIENDS
??
MATCH(b)-[]-(a)-[]-(c)
WHERE a.name = {name}
WHERE NOT (b)-[]-(c)
...

PRESENTED
BY
What’s new?
Graph-Aided Search
+

PRESENTED
BY
a graph local query that counts the number of nodes a single start node (seed) is
connected to at a certain depth, and only counts nodes that are k-hops away.
Benchmarking RedisGraph
K-hop neighborhood count query
k=1, c=2 k=2, c=3

PRESENTED
BY
Benchmarking RedisGraph
Query
k ∈[1,2,3,6]
seed ∈ {100K random
deterministic seeds}

PRESENTED
BY
Performance RedisGraph v1.2 vs 2.0
up to 3.7x more ops/sec
highly concurrent and
compute intensive
datasets benefit the most!

PRESENTED
BY
Performance RedisGraph v1.2 vs 2.0
up to 3.9x faster queries
highly concurrent and
compute intensive
datasets benefit the most!

PRESENTED
BY
• Thorough academic research
• Industry standard
• v2.0 supports the majority of test cases
New benchmark support going forward
LDBC

PRESENTED
BY
Partners | Integrations | Ecosystem

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The document discusses the challenges of building a highly available persistent session management service. It describes Zulily's legacy architecture which lacked high availability and required manual intervention. A new architecture is proposed using Redis for persistent storage, Dynomite for real-time replication across data centers, and a connection pooling proxy to improve efficiency and distribute load. The architecture provides high availability through replication, reduces overhead through connection pooling, and handles failures through consistent hashing and health checks. It was tested through simulations and showed a failure rate of only 0.42% during outages.

Anatomy of a Redis Command by Madelyn Olson of Amazon Web Services - Redis Da...Redis Labs

The document describes the process that a Redis command follows from the client side to the server side. On the client side, the command is sent over the network to the Redis server. On the server side, the command is read from the kernel buffers, validated, executed by calling the relevant command handler, and the response is written back to the client over the network. The core functions involved on the server side are ReadQueryFromClient(), ProcessInputBuffer(), ProcessCommand(), Call(), and handleClientsWithPendingWrites(). Redis 6.0 introduced I/O threads to handle reads and writes in parallel for improved performance while still maintaining Redis' single-threaded processing model.

Building a Multi-dimensional Analytics Engine with RedisGraph by Matthew Goos...Redis Labs

This document discusses MDmetrix, a healthcare data intelligence company that uses RedisGraph to provide flexible analysis of hospital data. RedisGraph is a graph database that represents data as nodes and relationships and uses an adjacency matrix and linear algebra to query the graph. MDmetrix models its healthcare data as a property graph in RedisGraph to allow for complex queries across different data dimensions like patients, facilities, procedures and drugs. RedisGraph allows MDmetrix to query the data more easily than traditional OLAP cubes or relational databases due to the semi-structured and flexible nature of the graph model.

RedisTimeSeries 1.2 by Pieter Cailliau - Redis Day Bangalore 2020Redis Labs

RedisTimeSeries is a time-series database that provides compression to reduce memory usage by up to 98% and improve performance. The RedisTimeSeries 1.2 release includes compression algorithms based on a Facebook paper that provide stable ingestion times independent of the number of data points. It also includes a reviewed API with performance improvements and clearer functionality. Performance testing showed ingestion throughput improved by 2-3% and query performance increased from 15-70% with the new release compared to the previous version.

RedisAI 0.9 by Sherin Thomas of Tensorwerk - Redis Day Bangalore 2020Redis Labs

This document summarizes RedisAI 0.9 and its capabilities for model deployment and benchmarking. It introduces RedisAI's new tensor data type and ability to deploy models to CPU and GPU. It then discusses AIBench, a tool developed to benchmark AI serving solutions like RedisAI, TensorFlow Serving, and REST APIs. The benchmarks show RedisAI providing 5.5x and 2.5x more inferences than REST APIs and TensorFlow Serving respectively, due to its data locality. The document concludes by mentioning RedisAI's integration with MLFlow for model deployment with a single command.

Rate-Limiting 30 Million requests by Vijay Lakshminarayanan and Girish Koundi...Redis Labs

The document discusses how Freshworks uses Redis Labs to rate limit 30 million API requests per day through their API gateway called Fluffy. Fluffy stores rate limit policies and maintains counters to track requests. Redis Labs allows Fluffy to easily scale to handle the high volume of requests by providing a fast, in-memory data store for managing rate limiting counters. The system was able to successfully rate limit 30 million requests per day with Redis Labs.

Three Pillars of Observability by Rajalakshmi Raji Srinivasan of Site24x7 Zoh...Redis Labs

Solving Complex Scaling Problems by Prashant Kumar and Abhishek Jain of Myntr...Redis Labs

Redis was used by Myntra to solve several complex scaling problems. It was used to build a scalable user segment service to support high read throughput of up to 5 million requests per minute with low latency. Redis allowed the service to scale beyond a single instance and included features like automatic backups and memory management. Redis also helped build a scalable mobile verification platform to reliably handle 100,000 requests per minute and scale to support higher future volumes. It was used as both a transient store and persistent backend. Finally, Redis locks helped build a scalable A/B testing platform by allowing experiments to be created and updated in an orderly concurrent fashion.

Redis as a High Scale Swiss Army Knife by Rahul Dagar and Abhishek Gupta of G...Redis Labs

This document discusses how Redis is used as a high-performance data store and messaging broker to power various services and personalization features at Goibibo, a leading online travel agency in India. Some key ways Redis is used include caching website content to improve performance, powering probabilistic models for personalization, acting as a broker for asynchronous background tasks, storing real-time user behavior signals to power adaptive features, and powering location-based services. Redis provides high throughput, reliability and various data structures to meet Goibibo's needs.

Deploying Redis as a Sidecar in Kubernetes by Janakiram MSV - Redis Day Banga...Redis Labs

This document discusses deploying Redis as a sidecar in Kubernetes. It outlines installing Redis in Kubernetes, different Redis deployment patterns for microservices including ambassador sidecar and sidecar patterns, and demonstrates deploying Redis as a sidecar to accelerate application performance by co-locating web and cache pods. The agenda includes an overview of Redis and Kubernetes, Redis deployment patterns, and applying the sidecar pattern to deploy Redis alongside applications in Kubernetes pods.