Add figures

Author: wangkuiyi

README.md

@@ -1,17 +1,15 @@
 # Provision SQLFlow Desktop for Linux
 
-I use Ubuntu 18.04 as the base Vagrant image.  The following commands
-download the VM image, starts a VM, and SSH into this VM.
+This is an experimental work to check deploying the whole SQLFlow
+service mesh on a Ubuntu 18.04 VM.
 
-```bash
-vagrant init ubuntu/bionic64
-vagrant up
-vagrant ssh
-```
+The general architecture of SQLFlow is as the following:
 
-In the VM, I need to install Docker.
+![](figures/arch.svg)
 
-```bash
-sudo apt update
-sudo apt install -y docker.io
-```
+In this deployment, we have Jupyter Notebook server, SQLFlow server,
+and MySQL running in a container executing the
+`sqlflow/sqlflow:latest` image.  Argo runs on a minikube cluster
+running on the VM.  The deployment is shown in the folllowing figure:
+
+![](figures/arch_vm.svg)

figures/arch.dot

@@ -0,0 +1,37 @@
+digraph G {
+        node [shape=box];
+
+        User1 [shape=oval, label="Lily"];
+        User2 [shape=oval, label="Bob"];
+        User3 [shape=oval, label="Eva"];
+
+        {rank = same; User1; User2; User3}
+
+        Browser1 [label="Web browser"];
+        Browser2 [label="Web browser"];
+
+        {rank = same; Browser1, Browser2, Client}
+
+        Jupyter [label="Jupyter Notebook server +\n SQLFlow magic command"];
+        SQLFlow [label="SQLFlow server"];
+        Argo [label="Tekton on Kubernetes\n(each workflow step is a container)"];
+        AI [label="AI engine\n(Alibaba PAI, KubeFlow+Kuberntes, etc)"];
+        DBMS [label="database system\n(Hive, MySQL, MaxCompute, etc)"];
+
+        User1 -> Browser1;
+        User2 -> Browser2;
+        Browser1 -> Jupyter [label="SQL/Flow program"];
+        Browser2 -> Jupyter;
+
+        Jupyter -> SQLFlow [label="SQL/Flow program"];
+        SQLFlow -> Argo [label="Argo workflow"];
+        Argo -> DBMS [label="submit SQL statement"];
+        Argo -> AI [label="submit AI job"];
+
+        SQLFlow -> DBMS [label="verify data schema"];
+
+        Client [label="sqlflow command-line client"];
+
+        User3 -> Client;
+        Client -> SQLFlow [label="SQL/Flow program"];
+}

figures/arch.svg

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+digraph G {
+        node [shape=box];
+
+        User1 [shape=oval, label="Lily"];
+        User2 [shape=oval, label="Bob"];
+        User3 [shape=oval, label="Eva"];
+
+        {rank = same; User1; User2; User3}
+
+        Browser1 [label="Web browser"];
+        Browser2 [label="Web browser"];
+
+        {rank = same; Browser1, Browser2, Client}
+
+        subgraph cluster_vm {
+                label="VM"
+                subgraph cluster_container {
+                        label="sqlflow/sqlflow:latest";
+                        Jupyter [label="Jupyter Notebook server +\n SQLFlow magic command"];
+                        SQLFlow [label="SQLFlow server"];
+                        DBMS [label="MySQL)"];
+                }
+                Argo [label="minikube + Tekton"];
+                AI [label="AI engine\n(Alibaba PAI, KubeFlow+Kuberntes, etc)"];
+        }
+
+        User1 -> Browser1;
+        User2 -> Browser2;
+        Browser1 -> Jupyter [label="SQL/Flow program"];
+        Browser2 -> Jupyter;
+
+        Jupyter -> SQLFlow [label="SQL/Flow program"];
+        SQLFlow -> Argo [label="Argo workflow"];
+        Argo -> DBMS [label="submit SQL statement"];
+        Argo -> AI [label="submit AI job"];
+
+        SQLFlow -> DBMS [label="verify data schema"];
+
+        Client [label="sqlflow command-line client"];
+
+        User3 -> Client;
+        Client -> SQLFlow [label="SQL/Flow program"];
+}