I have been able to successfully run apache ignite with custom config using the command
docker run -it --net=host -v "pathToLocalDirectory"/config:/opt/ignite/apache-ignite/config -e "CONFIG_URI=file:///opt/ignite/apache-ignite/config/default-config.xml" apacheignite/ignite.
But when I run my java project in IntelliJ I get the message
"IP finder returned empty addresses list. Please check IP finder configuration and make sure multicast works on your network...".
Note: the java client project works if I run the ignite server using windows batch file.
Also, I have published 47500 port as well. the result is the same.
try running it using docker -run -it --net=host (don't mount the volumes).
If that doesn't work, it means that either something is incorrect w/your docker setup OR you are configuring discovery differently for clients and servers.
check the IP addresses listed in your client discovery section.
ssh into the container and check what is actually mounted?
run docker exec -it container-name /bin/bash
check: /opt/ignite/apache-ignite/config/default-config.xml is there and contains the correct discovery info.
Check that the ignite log (located in /opt/ignite/apache-ignite/work/log/) specifies that the correct config is being used.
It will have a line like so: [INFO][main][IgniteKernal] Config URL: file:/opt/ignite/apache-ignite/config/default-config.xml
If you don't see the mounted config file try mounting more simply.
docker run -d -v /local/dir/config.xml:/config-file.xml -e CONFIG_URI=/config-file.xml apacheignite/ignite
more info:
https://apacheignite.readme.io/docs/docker-deployment
https://apacheignite.readme.io/docs/tcpip-discovery
I am trying to make a neo4j container work using the command below. The command was adjusted from the documentation.
docker run --detach \
-p7474:7474 \
-p7687:7687 \
-v $HOME/neo4j/data:/data \
-v $HOME/neo4j/logs:/logs \
-v $HOME/neo4j/conf:/conf \
--env NEO4J_AUTH=none \
neo4j
When the container starts, I am trying to interact with the database via the browser UI (http://localhost:7474).
The issue is that when I try to perform database related actions I get a ServiceUnavailable error (after minutes of waiting) with the message:
Could not perform discovery. No routing servers available. Known routing table: RoutingTable[database=default database, expirationTime=0, currentTime=1583932006016, routers=[], readers=[], writers=[]]
This happens for any cypher statement I run. For example:
MATCH (n)
I've searched for solutions for this error, but none seems applicable (as most relate to cluster setup or calling neo4j services externally).
From the conf file I could not find anything which might help (where the only change I performed was uncommenting:
# To accept non-local connections, uncomment this line:
dbms.default_listen_address=0.0.0.0
Question: How can I fix this issue so that I can interact with the database via the neo4j interface?
Any input is much apreciated.
Make sure you are pointing to your correct and complete folder locations....
e.g -v $HOME/neo4j/data:/Users/xxx/Downloads/data
I downloaded 2 versions of neo4j on Ubuntu 18.04 which are "neo4j-community-3.5.12" and "neo4j-community-3.5.8"
I run 3.5.8 with default settings I can see it from the web. http://localhost:7474/
For 3.5.12 I changed conf/neo4j.conf file and set some other port numbers for not to conflict with the default ones.
3.5.8 version runs fine on :7474. When I start 3.5.12, the logs says it is running but when I check from browser it is not running. I tried 2 different port settings, none worked. Below is the log file.
Why it is not running?
I see that many people recommended using docker. I also tried that.
I set up docker a container with command
sudo docker run --name db1 -p7474:7474 -p7687:7687 -d -v /db1/data:/data -v /db1/logs:/logs -v /db1/conf:/conf --env NEO4J_AUTH=none neo4j
here I have an existing /d1/data/databases/graph.db folder. When I go to localhost:7474 it is fine it shows me the existing database.
I set up another docker container with command
sudo docker run --name db2 -p3001:7474 -p3002:7473 -p3003:7687 -d -v /db2/data:/data -v /db2/logs:/logs -v /db2/conf:/conf --env NEO4J_AUTH=none neo4j
here I expect to see an EMPTY database but I see the already existing database again. When I go to the data folder inside db2. I see that it created some files here. WHY do I see the same database?
Also note that when I go to see the databases, headers of the web pages showing they are using the same bolt port?
can I copy the neo4j image and use different images to generate containers? Does that help?
I recognized that multiple databases are running and active but somehow I'm not able to reach the second one through a browser.
Considering the docker commands-
cmd1: sudo docker run --name db1 -p7474:7474 -p7687:7687 -d -v /db1/data:/data -v /db1/logs:/logs -v /db1/conf:/conf --env NEO4J_AUTH=none neo4j
cmd2: sudo docker run --name db2 -p3001:7474 -p3002:7473 -p3003:7687 -d -v /db2/data:/data -v /db2/logs:/logs -v /db2/conf:/conf --env NEO4J_AUTH=none neo4j
The container ports are defaults exposed as the same host port for db1 instance. Whereas for db2 instance series 3xxx has been used.
To browse the UI provided by neo4j, you can use either 7474 or 3001 port which is mapped to 7474 container port.
Neo4j browser uses defaults (from neo4j.conf) to connect to neo4j server. The default settings are as
bolt://<machineip>:7687, where db1 instance has already exposed the container port to 7687 host port.
A running instance is found on 7687 port which initiates a WebSocket connection for db1 and db2.
How to connect to an appropriate instance?
Use: :server disconnect and :server connect with the appropriate bolt://<machineip>:port connection string
Map db1 instance bolt container port to different host port (i.e. other than 7687)
As no defaults will be available
(Preferred), set the same hostport:containerport combination e.g.
cmd2: sudo docker run --name db2 -p3001:7474 -p3002:7473 -p3003:3003-d -v /db2/data:/data -v /db2/logs:/logs -v /db2/conf:/conf --env NEO4J_AUTH=none neo4j
in this case, a Volume has to be mapped to provide neo4j.conf with updated values as dbms.connector.bolt.listen_address=:3003
In case anybody still needs it: Here is how to run two neo4j databases neo4j_01 and neo4j_02 in two different docker containers, saving the data in different directories and accessing them on different ports.
docker container 1: neo4j_01
docker run \
--name neo4j_01 \
-p1474:7474 -p1687:7687 \
-d \
-v $HOME/neo4j_01/neo4j/data:/data \
-v $HOME/neo4j_01/neo4j/logs:/logs \
-v $HOME/neo4j_01/neo4j/import:/var/lib/neo4j/import \
-v $HOME/neo4j_01/neo4j/plugins:/plugins \
--env NEO4J_AUTH=username/enterpasswordhere \
neo4j:latest
docker container 2: neo4j_02
docker run \
--name neo4j_02 \
-p2474:7474 -p2687:7687 \
-d \
-v $HOME/neo4j_02/neo4j/data:/data \
-v $HOME/neo4j_02/neo4j/logs:/logs \
-v $HOME/neo4j_02/neo4j/import:/var/lib/neo4j/import \
-v $HOME/neo4j_02/neo4j/plugins:/plugins \
--env NEO4J_AUTH=username/enterpasswordhere \
neo4j:latest
After executing the code above e.g. neo4j_01 can be reached on port 1474 (when logging in you need to change the bolt port to 1687 in the first line and then enter username and password in second and third line)
You can stop a container with docker kill neo4j_01 and restart it with docker start neo4j_01. Data will still be there. It is saved in $HOME/neo4j_01/neo4j/data.
Doing it like this, I did not encounter any problems with ports/ accessing the wrong database etc.
After a lot of effort, my solution is not to use docker.
Go and download a community server from here. https://neo4j.com/download-center/#community. It will give you a compressed file. Extract it. You will have a folder named like neo4j-community-3.5.14. Make a copy of THAT FOLDER. For each server instance, make a copy.
Inside the folder, there is a conf folder which has a file named neo4j.conf. Open that file. By changing some settings inside this folder, you can run many neo4j servers. Change the below settings
To accept non-local connections, uncomment this line:
dbms.connectors.default_listen_address=0.0.0.0
change some port numbers so that they won't intersect with already used ones
dbms.connector.bolt.listen_address=:3003
dbms.connector.https.listen_address=:3002
dbms.connector.http.listen_address=:3001
Many questions exists about how to run containers in detached mode.
My question though is kinda specific to running Atlassian Bitbucket server in detached mode containers.
I tried the below as the last layer in my dockerfile and when i run the container with -d the process is not started
RUN /opt/atlassian-bitbucket/bin/start-bitbucket.sh
I tried using ENTRYPOINT like below
ENTRYPOINT ["/opt/atlassian-bitbucket/bin/start-bitbucket.sh"]
but container always exits after the start script completes.
Not sure if anyone has setup a Bitbucket Data center in containers but i am curious to see how they would have run multiple containers of the same image and made them join a single cluster.
Full disclosure: I work for Atlassian Premier Support, work closely with our Bitbucket Server team, and have been the primary maintainer of the atlassian/bitbucket-server Docker image for the past couple of years.
Short version
First: Use our official image, there are a host of problems we've solved over the years so rather than trying to start from scratch use ours as a base.
Second: You can indeed run a Data Center cluster in Docker. My personal test environment consists of 3 cluster nodes and a couple of Smart Mirrors, all using the official image, with HAProxy in front acting as a load balancer and an external Elasticsearch instance managing search. Check out the README above for a list of common configuration options - the ones you'll likely need can be set by passing environment variables
Long Version
AKA "How can I spin up a full DC cluster in a test environment?"
Here's a simple tutorial I put together for our own internal support teams a long time ago. It uses a custom HAProxy Docker container to give you an out-of-the-box load balancer. It's intended for testing on a single host, so if you want to do something different or closer to a production deployment, this won't cover that.
There's a lot to cover here, so let's start with the basics.
Networking
There are a few ways to connect up individual Docker containers so they can find each other and communicate (e.g. the --link parameter), but a Docker Network is by far the most flexible. With a dedicated network, we get the following:
Inter-container communication: Containers on the same network can communicate with each other and access services from other containers without the need to publish specific ports to the host.
Automatic DNS: Containers can find each other via their container name (defined by the --name parameter). Unlike real DNS however, when a container is down its DNS resolution ceases to exist. This can cause some issues for services like HAProxy - but we'll get to that later. Also worth noting is that this does not set the machine's hostname, which needs to be set separately if required.
Static IP assignment: For certain use cases it's useful to give Docker containers static IP addresses within their network
Multicast: Docker networks support multicast by default, which is perfect for Data Center nodes communicating over Hazelcast
One thing a Docker network doesn't do is attach the host to its network, so you, the user, can't connect to containers by container name, and you still need to publish ports to the local machine. However, there are situations where doing this is both useful and necessary. The simplest workaround is to add entries to your hosts file that point each container name you wish to access to the loopback address, 127.0.0.1
To create a Docker network, run the following command. In my example we're going to name our network atlasNetwork. If you want to use another name, remember to change the network name in all subsequent docker commands.
docker network create --driver bridge \
--subnet=10.255.0.0/16 \
atlasNetwork
Here, we're creating a network using the bridge driver - this is the simplest type of network. More complex network types allow the network to span multiple hosts. We're also manually specifying the subnet - if we leave this out Docker will choose one at random, and it could conflict with an existing network subnet, so it's safest to choose our own. We're also specifying a /16 mask to allow us to use IP address ranges within the last two octets - this will come up later!
Storage
Persistent data such as $BITBUCKET_HOME, or your database files, need to be stored somewhere outside of the container itself. For our test environment, we can simply store these directly on the host, our local OS. This means we can edit config files using our favourite text editor, which is pretty handy!
In the examples below, we're going to store our data files in the folder ~/dockerdata. There's no need to create this folder or any subfolders, as Docker will do this automatically. If you want to use a different folder, make sure to update the examples below.
You may wonder why we're not using Docker's named volumes instead of mounting folders on the host. Named volumes are an easier to manage abstraction and are generally recommended; however for the purposes of a test environment (particularly on Docker for Mac, where you don't have direct access to the virtualised file system) there's a huge practical benefit to being able to examine each container's persistent data directly. You may want to edit a number of configuration files in Bitbucket, or Postgres, or HAProxy, and this can be difficult when using a named volume, as it requires you to open a shell into the container - and many containers don't contain basic text editor utilities (not even vi!). However, if you prefer to use volumes, you can do so simply by replacing the host folder with the named volume in all of the below examples.
Database
The first service we need on our network is a database. Let's create a Postgres instance:
docker run -d \
--name postgres \
--restart=unless-stopped \
-e POSTGRES_PASSWORD=mysecretpassword \
-e PGDATA=/var/lib/postgresql/data/pgdata \
-v ~/dockerdata/postgres:/var/lib/postgresql/data/pgdata \
--network=atlasNetwork \
-p 5432:5432 \
postgres:latest
Let's examine what we're doing here:
-d
Run the container and detach from it (return to the prompt). Without this option, when the container starts we'll be attached directly to its stdout, and cancelling out would stop the container.
--name postgres
Set the name of the container to postgres, which also acts as its DNS record on our network.
--restart=unless-stopped
Sets the container to automatically start when Docker starts, unless you have explicitly stopped the container. This way, when you restart your computer, Postgres comes back up automatically
-e POSTGRES_PASSWORD=mysecretpassword
Sets password for the default postgres user to mysecretpassword
-e PGDATA=/var/lib/postgresql/data/pgdata
The official Postgres docker image recommends specifying this custom location when mounting the data folder to an external volume
-v ~/dockerdata/postgres:/var/lib/postgresql/data/pgdata
Mounts the folder /var/lib/postgresql/data/pgdata inside the container to an external volume, located on the host at ~/dockerdata/postgres. This folder will be created automatically
--network=atlasNetwork
Joins the container to our custom Docker network
-p 5432:5432
Publishes the Postgres port to the host machine, so we can access Postgres on localhost:5432. This isn't necessary for other containers to access the service, but it is necessary for us to get to it
postgres:latest
The latest version of the official Postgres docker image
Run the command, and hey presto, you can now access a fully functioning Postgres instance. For the sake of consistency, you may want to add your very first hosts entry here:
127.0.0.1 postgres
Now you, and any running containers, can access the instance at postgres:5432
Before you move on, you should connect to your database using your DB admin tool of choice. Connect to the hostname postgres with the username postgres, the default database postgres and the password mysecretpassword, and create a Bitbucket database ready to go:
CREATE USER bitbucket WITH PASSWORD 'bitbucket';
CREATE DATABASE bitbucket WITH OWNER bitbucket ENCODING 'UTF8';
If you don't have a DB admin tool handy, you can create a DB by using docker exec to run psql directly in the container:
# We need to run two commands because psql won't let
# you run CREATE DATABASE from a multi-command string
docker exec -it postgres psql -U postgres -c \
"CREATE USER bitbucket WITH PASSWORD 'bitbucket';"
docker exec -it postgres psql -U postgres -c \
"CREATE DATABASE bitbucket WITH OWNER bitbucket ENCODING 'UTF8';"
Elasticsearch
The next service we'll set up is Elasticsearch. We need a dedicated instance that all of our Data Center nodes can access. We have a great set of instructions on how to install a compatible version, configure it for use with Bitbucket, and install Atlassian's buckler security plugin: Install and configure a remote Elasticsearch instance
So how do we set this up in Docker? Well, it's easy:
docker pull dchevell/bitbucket-elasticsearch:latest
docker run -d \
--name elasticsearch \
-e AUTH_BASIC_USERNAME=bitbucket \
-e AUTH_BASIC_PASSWORD=mysecretpassword \
-v ~/dockerdata/elastic:/usr/share/elasticsearch/data \
--network=atlasNetwork \
-p 9200:9200 \
dchevell/bitbucket-elasticsearch:latest
Simply put, dchevell/bitbucket-elasticsearch is a pre-configured Docker image that is set up according to the instructions on Atlassian's Install and configure a remote Elasticsearch instance KB article. Atlassian's Buckler security plugin is installed for you, and you can configure the username and password with the environment variables seen above. Again, we're mounting a data volume to our host machine, joining it to our Docker network, and publishing a port so we can access it directly. This is solely for troubleshooting purposes, so if you want to poke around in your local Elasticsearch instance without going through Bitbucket, you can.
Now we're done, you can add your second hosts entry:
127.0.0.1 elasticsearch
HAProxy
Next, we'll set up HAProxy. Installing Bitbucket Data Center provides some example configuration, and again, we have a pre-configured Docker image that does all the hard work for us. But first, there's a few things we need to figure out first.
HAProxy doesn't play well with a Docker network's DNS system. In the real world, if a system is down, the DNS record still exists and connections will simply time out. HAProxy handles this scenario just fine. But in a Docker network, when a container is stopped, its DNS record ceases to exist, and connections to it fail with an "Unknown host" error. HAProxy won't start when this happens, which means we can't configure it to proxy connections to our nodes by container name. Instead, we will need to give each node a static IP address, and configure HAProxy to use the IP address instead.
Even though we have yet to create our nodes, we can decide on the IP addresses for them now. Our Docker network's subnet is 10.255.0.0/16, and Docker will dynamically assign containers addresses on the last octet (e.g. 10.255.0.1, 10.255.0.2 and so on). Since we know this, we can safely assign our Bitbucket nodes static IP addresses using the second-last octet:
10.255.1.1
10.255.1.2
10.255.1.3
With that out of the way, there's one more thing. HAProxy is going to be the face of our instance, so its container name is going to represent the URL we use to access the instance. In this example, we'll call it bitbucketdc. We're also going to set the host name of the machine to be the same.
docker run -d \
--name bitbucketdc \
--hostname bitbucketdc \
-v ~/dockerdata/haproxy:/usr/local/etc/haproxy \
--network=atlasNetwork \
-e HTTP_NODES="10.255.1.1:7990,10.255.1.2:7990,10.255.1.3:7990" \
-e SSH_NODES="10.255.1.1:7999,10.255.1.2:7999,10.255.1.3:7999" \
-p 80:80 \
-p 443:443 \
-p 7999:7999 \
-p 8001:8001 \
dchevell/bitbucket-haproxy:latest
In the above example, we're specifying the HTTP endpoints of our future Bitbucket nodes, as well as the SSH endpoints, as a comma separated list. The container will turn this into valid HAProxy configuration. The proxied services will be available on port 80 and port 443, so we're publishing them both. This container is configured to automatically generate a self-signed SSL certificate based on the hostname of the machine, so we have HTTPS access available out of the box.
Since we're proxying SSH as well, we're also publishing port 7999, Bitbucket Server's default SSH port. You'll notice we're also publishing port 8001. This is to access HAProxy's Admin interface, so we can monitor which nodes are detected as up or down at any given time.
Lastly, we're mounting HAProxy's config folder to a data volume. This isn't really necessary, but it will let you directly access haproxy.cfg so you can get a feel for the configuration options there.
Now it's time for our third hosts entry. This one is , since it impacts things like Base URL access, is absolutely required
127.0.0.1 bitbucketdc
Bitbucket nodes
Finally we're ready to create our Bitbucket nodes. Since these are all going to be accessed via the load balancer, we don't have to publish any ports. However, for troubleshooting and testing purposes there are times when you'll want to hit a particular node directly, so we're going to publish each node to a different local port so we can access it directly when needed.
docker run -d \
--name=bitbucket_1 \
-e ELASTICSEARCH_ENABLED=false \
-e HAZELCAST_NETWORK_MULTICAST=true \
-e HAZELCAST_GROUP_NAME=bitbucket-docker \
-e HAZELCAST_GROUP_PASSWORD=bitbucket-docker \
-e SERVER_PROXY_NAME=bitbucketdc \
-e SERVER_PROXY_PORT=443 \
-e SERVER_SCHEME=https \
-e SERVER_SECURE=true \
-v ~/dockerdata/bitbucket-shared:/var/atlassian/application-data/bitbucket/shared \
--network=atlasNetwork \
--ip=10.255.1.1 \
-p 7001:7990 \
-p 7991:7999 \
atlassian/bitbucket-server:latest
docker run -d \
--name=bitbucket_2 \
-e ELASTICSEARCH_ENABLED=false \
-e HAZELCAST_NETWORK_MULTICAST=true \
-e HAZELCAST_GROUP_NAME=bitbucket-docker \
-e HAZELCAST_GROUP_PASSWORD=bitbucket-docker \
-e SERVER_PROXY_NAME=bitbucketdc \
-e SERVER_PROXY_PORT=443 \
-e SERVER_SCHEME=https \
-e SERVER_SECURE=true \
-v ~/dockerdata/bitbucket-shared:/var/atlassian/application-data/bitbucket/shared \
--network=atlasNetwork \
--ip=10.255.1.2 \
-p 7002:7990 \
-p 7992:7999 \
atlassian/bitbucket-server:latest
docker run -d \
--name=bitbucket_3 \
-e ELASTICSEARCH_ENABLED=false \
-e HAZELCAST_NETWORK_MULTICAST=true \
-e HAZELCAST_GROUP_NAME=bitbucket-docker \
-e HAZELCAST_GROUP_PASSWORD=bitbucket-docker \
-e SERVER_PROXY_NAME=bitbucketdc \
-e SERVER_PROXY_PORT=443 \
-e SERVER_SCHEME=https \
-e SERVER_SECURE=true \
-v ~/dockerdata/bitbucket-shared:/var/atlassian/application-data/bitbucket/shared \
--network=atlasNetwork \
--ip=10.255.1.3 \
-p 7003:7990 \
-p 7993:7999 \
atlassian/bitbucket-server:latest
You can see that we're specifying the static IP addresses we decided on when we set up HAProxy. It's up to you whether you add hosts entries for these nodes, or simply access their ports via localhost. Since no other containers need to access our nodes via host name, it's not really necessary, and I personally haven't bothered.
The official Docker image adds the ability to set a Docker-only variable, ELASTICSEARCH_ENABLED=false to prevent Elasticsearch from starting in the container. The remaining Hazelcast properties are natively supported in the official docker image, because Bitbucket 5 is based on Springboot and can automatically translate environment variables to their equivalent dot properties for us.
Turn it all on
Now we're ready to go!
Access your instance on https://bitbucketdc (or whatever name you chose). Add a Data Center evaluation license (You can generate a 30 day one on https://my.atlassian.com) and connect it to your Postgres database. Log in, then go to Server Admin and connect your Elasticsearch instance (remember, it's running on port 9200, so set the Elasticsearch URL to http://elasticsearch:9200 and use the username and password we configured when we created the Elasticsearch container.
Visit the Clustering section in Server Admin, and you should see all of the nodes there, demonstrating that Multicast is working and the nodes have found each other.
That's it! Your Data Center instance is fully operational. You can use it as your daily instance by shutting down all but one node, and simply use it as a single node test instance - then, whenever you need, turn on the additional nodes.
see official docker image: https://hub.docker.com/r/atlassian/bitbucket-server/
just run:
docker run -v /data/bitbucket:/var/atlassian/application-data/bitbucket --name="bitbucket" -d -p 7990:7990 -p 7999:7999 atlassian/bitbucket-server
you can also take a look at the official dockerfile: https://hub.docker.com/r/atlassian/bitbucket-server/dockerfile
If you use the command to spin up the bitbucket container, you'll get the message below after the build:
The path /data/bitbucket is not shared from the host and is not known to Docker. You can configure shared paths from Docker -> Preferences... -> Resources -> File Sharing.
I'm trying to setup Apache Superset for Clickhouse.
My understanding so far is that I need to install SQLAlchemy for Clickhouse
https://github.com/xzkostyan/clickhouse-sqlalchemy
I'm in Ubuntu 16.04 LTS, and using the Docker vanilla version of Clickhouse and of Superset:
https://store.docker.com/community/images/yandex/clickhouse-server
https://hub.docker.com/r/amancevice/superset/
without special settings
Any idea how I can bridge the two docker containers with clickhouse-sqlalchemy ?
Where and how in that case to install that?
(if you have sample command line that I can reuse that will be great)
You don't need to bridge them: what you want is a superset server (that you happen to be running via docker) to connect to a clickhouse database (that you also happen to be running via docker).
You also shouldn't need to install SQLAlchemy for Clickhouse: looking at the dockerfile at https://hub.docker.com/r/amancevice/superset/~/dockerfile/ that image has already sqlalchemy-clickhouse installed for you.
Your steps should be as follow:
When you docker run --detach --name superset [options] amancevice/superset you should have your superset instance running at http://localhost:8088/
Similarly, when you run $ docker run -d --name some-clickhouse-server --ulimit nofile=262144:262144 -v /path/to/your/config.xml:/etc/clickhouse-server/config.xml yandex/clickhouse-server you should end-up with a clickhouse instance that you can access via SQLAlchemy at something like clickhouse://default:#some-clickhouse-server/test
You'd need to modify that connection URI based on your config.xml - and you should be able to double-check that it works by connecting to it in your python console.
You should then be able to connect superset to your clickhouse db in the same way you'd connect to any other DB: by navigating into Superset's menu > Sources > Databases > [new]
Consider using already prepared and configured docker-compose.yml which included in Apache Superset (see https://github.com/apache/superset/blob/master/docker-compose.yml).
To work with Clickhouse should be installed sqlalchemy driver. There are two ones:
clickhouse-sqlalchemy by xzkostyan
sqlalchemy-clickhouse by cloudflare.
I recommend using clickhouse-sqlalchemy because it is actually supported and evolute, it supports both available protocols to interact with ClickHouse - HTTP and TCP (native protocol).
Let's connect to one of the public ClickHouse:
either Demo Yandex CH
docker run -it --rm yandex/clickhouse-client:latest \
--host gh-api.clickhouse.tech --user explorer -s
or Demo Altinity.Cloud CH
docker run -it --rm yandex/clickhouse-client:latest \
--host github.demo.trial.altinity.cloud -s --user demo --password demo
download source code from repo https://github.com/apache/superset
execute the commands
cd superset-master
docker-compose up
# open the new terminal
docker-compose exec superset bash /app/docker/docker-init.sh
docker-compose exec superset pip install clickhouse-sqlalchemy
docker-compose restart
wait for containers to be started and the web app to be built (see the console output, webpack should finish its work)
browse URL http://localhost:8088 (use credentials admin / admin)
add the database using one of the connection string:
# connection string for Demo Yandex ClickHouse
clickhouse+native://explorer#gh-api.clickhouse.tech/default?secure=true
# connection string for Demo Altinity.Cloud CH
clickhouse+native://demo:demo#github.demo.trial.altinity.cloud/default?secure=true
See also https://stackoverflow.com/a/66006784/303298.