I'm a beginner of databases and I want to deploy neo4j on Google Cloud Platform.
I can find something about deploying MongoDB on Google, but nothing about neo4j.
So I wonder does Google Cloud support neo4j?
Thanks!
Neo4j is an open source project that could run your own Linux machine.
You could just create a Google Compute Engine instance, and follow the tutorials on the web to setup your Neo4j.
like this one: Neo4j setup instruction
just follow the Linux part, and I suggest you to use Debian image to create your instance for Neo4j, because the command-line tool on Debian is most like to Ubuntu's one.
Updated answer from 2018.
Yes -- neo4j supports Google Cloud. Instructions can be found on their website. You can use a pre-built image and launch a single node instance, or multi-node clusters on GCP.
Related
I have downloaded Gremlin Server with an intention of being able to use Gremlin to traverse a Neo4j DB.
Now, speaking of the latter, it has to be somehow added to the Gremlin Server installation, but I have difficulty finding any up-to-date guidance on how to do that. There are a few posts here on SO describing various kinds of problems people run into, but no definitive solution, much less one for the current versions of both Tinkerpop and Neo4j.
Would appreciate specific links, tips etc.
Thanks!
There is a "TIP" describing Gremlin Server configuration in the TinkerPop reference documentation found here. Basically, you -install Neo4j dependencies:
bin/gremlin-server.sh install org.apache.tinkerpop neo4j-gremlin 3.3.4
then you edit your Gremlin Server YAML configuration file to connect to your database. Gremlin Server contains a sample file to get you started and is found the /conf directory of the installation. Of critical note is this entry:
graphs: {
graph: conf/neo4j-empty.properties}
It specifies the Neo4j configuration to use and the sample one that ships with Gremlin Server looks like this:
gremlin.graph=org.apache.tinkerpop.gremlin.neo4j.structure.Neo4jGraph
gremlin.neo4j.directory=/tmp/neo4j
gremlin.neo4j.conf.dbms.auto_index.nodes.enabled=true
gremlin.neo4j.conf.dbms.auto_index.relationships.enabled=true
As you can see, the configuration basically just passes through Neo4j specific configuration to Neo4j itself. Only the first two lines are TinkerPop options. In this case, it sets up Neo4j for embedded mode, meaning Neo4j runs within the Gremlin Server JVM. You can make Gremlin Server part of a Neo4j HA cluster with instructions found in the reference documentation here.
Note that you asked for "current" versions of both TinkerPop and Neo4j. While these instructions are current for TinkerPop, I'm afraid that the Neo4j version TinkerPop supports is well behind their latest release. It would be nice if someone had time to issue a pull request for that.
Having skimmed the Google Cloud Dataflow documentation, my impression is that worker VMs run a specific predefined Python 2.7 environment without any option to change that. Is it possible to provide a custom VM image for the workers (built with libraries, external commands that the particular application needs). Is it possible to run Python 3 on Gcloud Dataflow?
2021 Update
As of today, the answer to both of this questions is YES.
Python 3 is supported on Dataflow.
Custom container images are supported on Dataflow, see this SO answer, and this docs page.
Is it possible to provide a custom VM image for the workers (built with libraries, external commands that the particular application needs). Is it possible to run Python 3 on Gcloud Dataflow?
No and no to both questions. You're able to configure Compute Engine instance machine type and disk size for a Dataflow job, but you're not able to configure things like installed applications. Currently, Apache Beam does not support Python 3.x.
References:
https://cloud.google.com/dataflow/pipelines/specifying-exec-params
https://issues.apache.org/jira/browse/BEAM-1251
https://beam.apache.org/get-started/quickstart-py/
Python 3 support in to Apache Beam status:
https://beam.apache.org/roadmap/python-sdk/#python-3-support
You cannot provide a custom VM image for the workers, but you can provide a setup.py file to run custom commands and install libraries.
You can find more info about the setup.py file here:
https://beam.apache.org/documentation/sdks/python-pipeline-dependencies/
Custom containers are now supported on Dataflow.
How to connect or load data from Kafka to neo4j. I saw Mazerunner, DocManager in Neo4j documentation to connect Neo4j with Spark and MongoDB. Is there any such kind of projects related to Kafka and neo4j.
Thanks.
Please check this GitHub project. This is with Neo4j 2.2.3 KernelExtention.
Link: https://github.com/ducky427/neo-kafka
You may need to change the settings code for any 3.0.x version.
Another way, if you are using Neo4j Embedded version, you can simply integrate these through your custom API.
Do share, if you have found other ways to integrate.
I would like to use the Spark-graphX packages available to Neo4j through Mazerunner, however I am an analyst and not a software person. I am running Windows 7 on my laptop and Neo4j 2.3.0, and would like a step-by-step guide explaining how I can set-up Mazerunner for both Community & Enterprise. There's a lot of mention of dockers and containers, and I have no idea what these are, or how to set them up. Simple instructions would be of sooo much help! :)
Docker is primarily Operating System Level Visualization technology designed to run on Unix based systems (Linux,Mac,FreeBSD). Luckily Docker provides a Windows version that sort of does the same thing on Unix.
What happens is, after you have installed Docker, it allows you to run what they call containers which are basically virtual machines on top of your host (Windows 7 Running Docker). This allows you to run services like Neo4j in an isolated environment. Docker also allows you to download and install pre-configured, pre-compiled images of operating systems that usually provide some sort of service or have some software pre-installed.
In your case, I believe all you have to do is:
First install Docker
Use "Docker Compose" to download and install the images.
Continue Reading the Tutorial as you have now installed the required docker images
Note: Some of the operations, like the one in Step 2 will require command-line access and Also the creation of a "docker-compose.yml" so, be sure to visit all the links I have provided. Spend a little time going through them and you should be alright.
PS: great blog. definitely bookmarking it!
So ive been looking through the init.d scripts and the bashrc file and cannot find where the microconsole binary is started on initial login. Can anyone tell me where it is?
Also, does MCF support running two microconsole instances at a time? My IaaS provider only supports SSH.
I am not entirely sure what you are trying to achieve here. Your trying to host MCF with an IaaS provider? like EC2 for example? If this is the case, I would recommend installing VCAP, MCF is not kept as up to date as the VCAP project.
It's pretty straight forward to install on Ubuntu 10.04, there are instructions on the Github project page, here ... https://github.com/cloudfoundry/vcap