Redis is phasing out RedisGraph. This blog post explains the motivation behind this decision and the implications for existing Redis customers and community members.
End of support is scheduled for January 31, 2025.
RedisGraph is the fastest graph database that processes complex graph operations in real time, 10x – 600x faster than any other graph database. It shows how your data is connected through multiple visualization integrations including RedisInsight, Linkurious, and Graphileon. It allows you to query graphs using the industry-standard Cypher query language and you can easily use graph capabilities from application code.
If you have a bunch of CSV files that you want to load to RedisGraph database, you must try out this Bulk Loader utility. Rightly called RedisGraph Bulk Loader, this tool is written in Python and helps you in building RedisGraph databases from CSV inputs. This utility requires a Python 3 interpreter.
Follow the steps below to load CSV data into RedisGraph database:
The bulk loader can be installed using pip:
Or
If the above command doesn’t work, install the below modules:
To use RedisInsight on a local Mac, you can download from the RedisInsight page on the RedisLabs website:
Click this link to access a form that allows you to select the operating system of your choice.
If you have Docker Engine installed in your system, the quick way is to run the following command:
Next, point your browser to http://localhost:8001.
docker run -p 6379:6379 --name redis/redis-stack info modules
# Modules
module:name=graph,ver=20405,api=1,filters=0,usedby=[],using=[],options=[] $ git clone https://github.com/RedisGraph/redisgraph-bulk-loader pip3 install redisgraph-bulk-loader pip3 install git+https://github.com/RedisGraph/redisgraph-bulk-loader.git@master python3 -m venv redisgraphloader source redisgraphloader/bin/activate pip3 install -r requirements.txt pip3 install pathos
pip3 install redis
pip3 install click groovy generateCommerceGraphCSVForImport.groovy head -n2 *.csv
==> addtocart.csv <==
src_person,dst_product,timestamp
0,1156,2010-07-20T16:11:20.551748
==> contain.csv <==
src_person,dst_order
2000,1215
==> order.csv <==
_internalid,id,subTotal,tax,shipping,total
2000,0,904.71,86.40,81.90,1073.01
==> person.csv <==
_internalid,id,name,address,age,memberSince
0,0,Cherlyn Corkery,146 Kuphal Isle South Jarvis MS 74838-0662,16,2010-03-18T16:25:20.551748
==> product.csv <==
_internalid,id,name,manufacturer,msrp
1000,0,Sleek Plastic Car,Thiel Hills and Leannon,385.62
==> transact.csv <==
src_person,dst_order
2,2000
==> view.csv <==
src_person,dst_product,timestamp
0,1152,2012-04-14T11:23:20.551748 python3 bulk_insert.py prodrec-bulk -n person.csv -n product.csv -n order.csv -r view.csv -r addtocart.csv -r transact.csv -r contain.csv
person [####################################] 100%
1000 nodes created with label 'person'
product [####################################] 100%
1000 nodes created with label 'product'
order [####################################] 100%
811 nodes created with label 'order'
view [####################################] 100%
24370 relations created for type 'view'
addtocart [####################################] 100%
6458 relations created for type 'addtocart'
transact [####################################] 100%
811 relations created for type 'transact'
contain [####################################] 100%
1047 relations created for type 'contain'
Construction of graph 'prodrec-bulk' complete: 2811 nodes created, 32686 relations created in 1.021761 seconds graph.query prodrec "match (p:person) where p.id=200 return p.name"
1) 1) "p.name"
2) (empty array)
3) 1) "Cached execution: 0"
2) "Query internal execution time: 0.518300 milliseconds" docker run -d -v redisinsight:/db -p 8001:8001 redislabs/redisinsight:latest GRAPH.QUERY "prodrec-bulk" "match (p:person) where p.id=199 return p"