Privacy Preserving Machine Learning (PPML) on Azure User Guide

1. Introduction

Protecting privacy and confidentiality is critical for large-scale data analysis and machine learning. BigDL PPML combines various low-level hardware and software security technologies (e.g., Intel® Software Guard Extensions (Intel® SGX), Library Operating System (LibOS) such as Graphene and Occlum, Federated Learning, etc.), so that users can continue to apply standard Big Data and AI technologies (such as Apache Spark, Apache Flink, Tensorflow, PyTorch, etc.) without sacrificing privacy.

BigDL PPML on Azure solution integrate BigDL PPML technology with Azure Services(Azure Kubernetes Service, Azure Storage Account, Azure Key Vault, etc.) to faciliate Azure customer to create Big Data and AI applications while getting high privacy and confidentiality protection.

Overall Architecture

../../../_images/ppml_azure_latest.png

End-to-End Workflow

../../../_images/ppml_azure_workflow.png

2. Setup

2.1 Install Azure CLI

Before you setup your environment, please install Azure CLI on your machine according to Azure CLI guide.

Then run az login to login to Azure system before you run following Azure commands.

2.2 Create Azure VM for hosting BigDL PPML image

2.2.1 Create Resource Group

On your machine, create resource group or use your existing resource group. Example code to create resource group with Azure CLI:

az group create \
    --name myResourceGroup \
    --location myLocation \
    --output none

2.2.2 Create Linux client with SGX support

Create Linux VM through Azure CLI/Portal/Powershell. For size of the VM, please choose DC-V3 Series VM with more than 4 vCPU cores.

2.2.3 Pull BigDL PPML image and run on Linux client

  • Go to Azure Marketplace, search “BigDL PPML” and find BigDL PPML product. Click “Create” button which will lead you to Subscribe page. On Subscribe page, input your subscription, your Azure container registry, your resource group, location. Then click Subscribe to subscribe BigDL PPML to your container registry.

  • Login to the created VM. Then login to your Azure container registry, pull BigDL PPML image using such command:

docker pull myContainerRegistry/bigdl-ppml-trusted-big-data-ml-python-graphene:2.1.0-SNAPSHOT
  • Start container of this image

#!/bin/bash

export LOCAL_IP=YOUR_LOCAL_IP
export DOCKER_IMAGE=intelanalytics/bigdl-ppml-trusted-big-data-ml-python-graphene:2.1.0-SNAPSHOT

sudo docker run -itd \
    --privileged \
    --net=host \
    --cpuset-cpus="0-5" \
    --oom-kill-disable \
    --device=/dev/gsgx \
    --device=/dev/sgx/enclave \
    --device=/dev/sgx/provision \
    -v /var/run/aesmd/aesm.socket:/var/run/aesmd/aesm.socket \
    --name=spark-local \
    -e LOCAL_IP=$LOCAL_IP \
    -e SGX_MEM_SIZE=64G \
    $DOCKER_IMAGE bash

2.3 Create AKS(Azure Kubernetes Services) or use existing AKS

Create AKS or use existing AKS with Intel SGX support.

In your BigDL PPML container, you can run /ppml/trusted-big-data-ml/azure/create-aks.sh to create AKS with confidential computing support.

Note: Please use same VNet information of your client to create AKS. And use DC-Series VM size(i.e.Standard_DC8ds_v3) to create AKS.

/ppml/trusted-big-data-ml/azure/create-aks.sh \
--resource-group myResourceGroup \
--vnet-resource-group myVnetResourceGroup \
--vnet-name myVnetName \
--subnet-name mySubnetName \
--cluster-name myAKSName \
--vm-size myAKSNodeVMSize \
--node-count myAKSInitNodeCount

You can check the information by run:

/ppml/trusted-big-data-ml/azure/create-aks.sh --help

2.4 Create Azure Data Lake Store Gen 2

2.4.1 Create Data Lake Storage account or use existing one.

The example command to create Data Lake store is as below:

az dls account create --account myDataLakeAccount --location myLocation --resource-group myResourceGroup
  • Create Container to put user data Example command to create container

az storage fs create -n myFS --account-name myDataLakeAccount --auth-mode login
  • Create folder, upload file/folder Example command to create folder:

az storage fs directory create -n myDirectory -f myFS --account-name myDataLakeAccount --auth-mode login

Example command to upload file

az storage fs file upload -s "path/to/file" -p myDirectory/file  -f  myFS --account-name myDataLakeAccount --auth-mode login

Example command to upload directory

az storage fs directory upload -f myFS --account-name myDataLakeAccount -s "path/to/directory" -d myDirectory --recursive

2.4.2 Access data in Hadoop through ABFS(Azure Blob Filesystem) driver

You can access Data Lake Storage in Hadoop filesytem by such URI: abfs[s]://file_system@account_name.dfs.core.windows.net/<path>/<path>/<file_name>

Authentication

The ABFS driver supports two forms of authentication so that the Hadoop application may securely access resources contained within a Data Lake Storage Gen2 capable account.

  • Shared Key: This permits users access to ALL resources in the account. The key is encrypted and stored in Hadoop configuration.

  • Azure Active Directory OAuth Bearer Token: Azure AD bearer tokens are acquired and refreshed by the driver using either the identity of the end user or a configured Service Principal. Using this authentication model, all access is authorized on a per-call basis using the identity associated with the supplied token and evaluated against the assigned POSIX Access Control List (ACL).

By default, in our solution, we use shared key authentication.

  • Get Access key list of storage account:

az storage account keys list -g MyResourceGroup -n myDataLakeAccount

Use one of the keys in authentication.

2.5 Create Azure Key Vault

2.5.1 Create or use an existing Azure Key Vault

Example command to create key vault

az keyvault create -n myKeyVault -g myResourceGroup -l location

Take note of the following properties for use in the next section:

  • The name of the secret object in the key vault

  • The object type (secret, key, or certificate)

  • The name of your Azure key vault resource

  • The Azure tenant ID that the subscription belongs to

2.5.2 Set access policy for the client VM

  • Run such command to get the system identity:

az vm identity assign -g myResourceGroup -n myVM

The output would be like this:

{
  "systemAssignedIdentity": "ff5505d6-8f72-4b99-af68-baff0fbd20f5",
  "userAssignedIdentities": {}
}

Take note of the systemAssignedIdentity of the client VM.

  • Set access policy for client VM Example command:

az keyvault set-policy --name myKeyVault --object-id <mySystemAssignedIdentity> --secret-permissions all --key-permissions all --certificate-permissions all

2.5.3 AKS access Key Vault

2.5.3.1 Set access for AKS VM ScaleSet

a. Find your VM ScaleSet in your AKS, and assign system managed identity to VM ScaleSet.
az vm identity assign -g myResourceGroup -n myAKSVMSS

The output would be like below:

principalId: xxxxxxxxx
tenantId: xxxxxxxxxxx
type: SystemAssigned, UserAssigned
userAssignedIdentities:
  ? /subscriptions/xxxx/resourceGroups/xxxxx/providers/Microsoft.ManagedIdentity/userAssignedIdentities/bigdl-ks-agentpool
  : clientId: xxxxxx
    principalId: xxxxx

Take note of principalId of the first line as System Managed Identity of your VMSS.

b. Set access policy for AKS VM ScaleSet

Example command:

az keyvault set-policy --name myKeyVault --object-id <systemManagedIdentityOfVMSS> --secret-permissions get --key-permissions all --certificate-permissions all

2.5.3.2 Set access for AKS

a. Enable Azure Key Vault Provider for Secrets Store CSI Driver support

Example command:

az aks enable-addons --addons azure-keyvault-secrets-provider --name myAKSCluster --resource-group myResourceGroup
  • Verify the Azure Key Vault Provider for Secrets Store CSI Driver installation Example command:

kubectl get pods -n kube-system -l 'app in (secrets-store-csi-driver, secrets-store-provider-azure)'

Be sure that a Secrets Store CSI Driver pod and an Azure Key Vault Provider pod are running on each node in your cluster’s node pools.

  • Enable Azure Key Vault Provider for Secrets Store CSI Driver to track of secret update in key vault

az aks update -g myResourceGroup -n myAKSCluster --enable-secret-rotation

b. Provide an identity to access the Azure Key Vault

There are several ways to provide identity for Azure Key Vault Provider for Secrets Store CSI Driver to access Azure Key Vault: An Azure Active Directory pod identity, user-assigned identity or system-assigned managed identity. In our solution, we use user-assigned managed identity.

  • Enable managed identity in AKS

az aks update -g myResourceGroup -n myAKSCluster --enable-managed-identity
  • Get user-assigned managed identity that you created when you enabled a managed identity on your AKS cluster Run:

az aks show -g myResourceGroup -n myAKSCluster --query addonProfiles.azureKeyvaultSecretsProvider.identity.clientId -o tsv

The output would be like:

f95519c1-3fe8-441b-a7b9-368d5e13b534

Take note of this output as your user-assigned managed identity of Azure KeyVault Secrets Provider

  • Grant your user-assigned managed identity permissions that enable it to read your key vault and view its contents Example command:

az keyvault set-policy -n myKeyVault --key-permissions get --spn f95519c1-3fe8-441b-a7b9-368d5e13b534
az keyvault set-policy -n myKeyVault --secret-permissions get --spn f95519c1-3fe8-441b-a7b9-368d5e13b534

c. Create a SecretProviderClass to access your Key Vault

On your client docker container, edit /ppml/trusted-big-data-ml/azure/secretProviderClass.yaml file, modify <client-id> to your user-assigned managed identity of Azure KeyVault Secrets Provider, and modify <key-vault-name> and <tenant-id> to your real key vault name and tenant id.

Then run:

kubectl apply -f /ppml/trusted-big-data-ml/azure/secretProviderClass.yaml

to create secretProviderClass in your AKS.

3. Run Spark PPML jobs

Login to your client VM and enter your BigDL PPML container:

docker exec -it spark-local bash

Then run az login to login to Azure system.

3.1 Generate enclave key to Azure Key Vault

Run such script to generate enclave key

/ppml/trusted-big-data-ml/azure/generate-enclave-key-az.sh myKeyVault

3.2 Generate keys

Run such scripts to generate keys:

/ppml/trusted-big-data-ml/azure/generate-keys.sh

When entering the passphrase or password, you could input the same password by yourself; and these passwords could also be used for the next step of generating other passwords. Password should be longer than 6 bits and contain numbers and letters, and one sample password is “3456abcd”. These passwords would be used for future remote attestations and to start SGX enclaves more securely.

3.3 Generate password

Run such script to save password to Azure Key Vault

/ppml/trusted-big-data-ml/azure/generate-password-az.sh myKeyVault used_password_when_generate_keys

3.4 Save kube config to secret

Login to AKS use such command:

az aks get-credentials --resource-group  myResourceGroup --name myAKSCluster

Run such script to save kube config to secret

/ppml/trusted-big-data-ml/azure/kubeconfig-secret.sh

3.5 Create the RBAC

kubectl create serviceaccount spark
kubectl create clusterrolebinding spark-role --clusterrole=edit --serviceaccount=default:spark --namespace=default

3.6 Run PPML spark job

The example script to run PPML spark job on AKS is as below. You can also refer to /ppml/trusted-big-data-ml/azure/submit-spark-sgx-az.sh

SPARK_EXTRA_JAR_PATH=
SPARK_JOB_MAIN_CLASS=
ARGS=
DATA_LAKE_NAME=
DATA_LAKE_ACCESS_KEY=
KEY_VAULT_NAME=
PRIMARY_KEY_PATH=
DATA_KEY_PATH=

LOCAL_IP=
RUNTIME_SPARK_MASTER=

secure_password=`az keyvault secret show --name "key-pass" --vault-name $KEY_VAULT_NAME --query "value" | sed -e 's/^"//' -e 's/"$//'`

export TF_MKL_ALLOC_MAX_BYTES=10737418240 && \
  /opt/jdk8/bin/java \
    -cp '/ppml/trusted-big-data-ml/work/spark-3.1.2/conf/:/ppml/trusted-big-data-ml/work/spark-3.1.2/jars/*' \
    -Xmx12g \
    org.apache.spark.deploy.SparkSubmit \
    --master $RUNTIME_SPARK_MASTER \
    --deploy-mode client \
    --name spark-decrypt-sgx \
    --conf spark.driver.host=$LOCAL_IP
    --conf spark.driver.memory=18g \
    --conf spark.driver.cores=2 \
    --conf spark.executor.cores=2 \
    --conf spark.executor.memory=24g \
    --conf spark.executor.instances=1 \
    --conf spark.driver.defaultJavaOptions="-Dlog4j.configuration=/ppml/trusted-big-data-ml/work/spark-3.1.2/conf/log4j2.xml" \
    --conf spark.executor.defaultJavaOptions="-Dlog4j.configuration=/ppml/trusted-big-data-ml/work/spark-3.1.2/conf/log4j2.xml" \
    --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
    --conf spark.kubernetes.container.image=intelanalytics/bigdl-ppml-trusted-big-data-ml-python-graphene:2.1.1-SNAPSHOT \
    --conf spark.kubernetes.driver.podTemplateFile=/ppml/trusted-big-data-ml/spark-driver-template-kv.yaml \
    --conf spark.kubernetes.executor.podTemplateFile=/ppml/trusted-big-data-ml/spark-executor-template-kv.yaml \
    --conf spark.kubernetes.executor.deleteOnTermination=false \
    --conf spark.network.timeout=10000000 \
    --conf spark.executor.heartbeatInterval=10000000 \
    --conf spark.python.use.daemon=false \
    --conf spark.python.worker.reuse=false \
    --conf spark.sql.auto.repartition=true \
    --conf spark.default.parallelism=400 \
    --conf spark.sql.shuffle.partitions=400 \
    --jars local://$SPARK_EXTRA_JAR_PATH \
    --conf spark.kubernetes.sgx.enabled=true \
    --conf spark.kubernetes.sgx.driver.mem=16g \
    --conf spark.kubernetes.sgx.driver.jvm.mem=7g \
    --conf spark.kubernetes.sgx.executor.mem=16g \
    --conf spark.kubernetes.sgx.executor.jvm.mem=7g \
    --conf spark.kubernetes.sgx.log.level=error \
    --conf spark.authenticate=true \
    --conf spark.authenticate.secret=$secure_password \
    --conf spark.kubernetes.executor.secretKeyRef.SPARK_AUTHENTICATE_SECRET="spark-secret:secret" \
    --conf spark.kubernetes.driver.secretKeyRef.SPARK_AUTHENTICATE_SECRET="spark-secret:secret" \
    --conf spark.authenticate.enableSaslEncryption=true \
    --conf spark.network.crypto.enabled=true \
    --conf spark.network.crypto.keyLength=128 \
    --conf spark.network.crypto.keyFactoryAlgorithm=PBKDF2WithHmacSHA1 \
    --conf spark.io.encryption.enabled=true \
    --conf spark.io.encryption.keySizeBits=128 \
    --conf spark.io.encryption.keygen.algorithm=HmacSHA1 \
    --conf spark.ssl.enabled=true \
    --conf spark.ssl.port=8043 \
    --conf spark.ssl.keyPassword=$secure_password \
    --conf spark.ssl.keyStore=/ppml/trusted-big-data-ml/work/keys/keystore.jks \
    --conf spark.ssl.keyStorePassword=$secure_password \
    --conf spark.ssl.keyStoreType=JKS \
    --conf spark.ssl.trustStore=/ppml/trusted-big-data-ml/work/keys/keystore.jks \
    --conf spark.ssl.trustStorePassword=$secure_password \
    --conf spark.ssl.trustStoreType=JKS \
    --conf spark.hadoop.fs.azure.account.auth.type.${DATA_LAKE_NAME}.dfs.core.windows.net=SharedKey \
    --conf spark.hadoop.fs.azure.account.key.${DATA_LAKE_NAME}.dfs.core.windows.net=${DATA_LAKE_ACCESS_KEY} \
    --conf spark.hadoop.fs.azure.enable.append.support=true \
    --conf spark.bigdl.kms.type=AzureKeyManagementService \
    --conf spark.bigdl.kms.azure.vault=$KEY_VAULT_NAME \
    --conf spark.bigdl.kms.key.primary=$PRIMARY_KEY_PATH \
    --conf spark.bigdl.kms.key.data=$DATA_KEY_PATH \
    --class $SPARK_JOB_MAIN_CLASS \
    --verbose \
    local://$SPARK_EXTRA_JAR_PATH \
    $ARGS

4. Run TPC-H example

TPC-H queries implemented in Spark using the DataFrames API running with BigDL PPML.

4.1 Generating tables

Go to TPC Download site, choose TPC-H source code, then download the TPC-H toolkits. After you download the tpc-h tools zip and uncompressed the zip file. Go to dbgen directory, and create a makefile based on makefile.suite, and run make.

This should generate an executable called dbgen

./dbgen -h

gives you the various options for generating the tables. The simplest case is running:

./dbgen

which generates tables with extension .tbl with scale 1 (default) for a total of rougly 1GB size across all tables. For different size tables you can use the -s option:

./dbgen -s 10

will generate roughly 10GB of input data.

4.2 Generate primary key and data key

Generate primary key and data key, then save to file system.

The example code of generate primary key and data key is like below:

java -cp '/ppml/trusted-big-data-ml/work/bigdl-2.1.0-SNAPSHOT/lib/bigdl-ppml-spark_3.1.2-2.1.0-SNAPSHOT-jar-with-dependencies.jar:/ppml/trusted-big-data-ml/work/spark-3.1.2/conf/:/ppml/trusted-big-data-ml/work/spark-3.1.2/jars/* \
   -Xmx10g \
   com.intel.analytics.bigdl.ppml.examples.GenerateKeys \
   --kmsType AzureKeyManagementService \
   --vaultName xxx \
   --primaryKeyPath xxx/keys/primaryKey \
   --dataKeyPath xxx/keys/dataKey

4.3 Encrypt Data

Encrypt data with specified BigDL AzureKeyManagementService

The example code of encrypt data is like below:

java -cp '/ppml/trusted-big-data-ml/work/bigdl-2.1.0-SNAPSHOT/lib/bigdl-ppml-spark_3.1.2-2.1.0-SNAPSHOT-jar-with-dependencies.jar:/ppml/trusted-big-data-ml/work/spark-3.1.2/conf/:/ppml/trusted-big-data-ml/work/spark-3.1.2/jars/* \
   -Xmx10g \
   com.intel.analytics.bigdl.ppml.examples.tpch.EncryptFiles \
   --kmsType AzureKeyManagementService \
   --vaultName xxx \
   --primaryKeyPath xxx/keys/primaryKey \
   --dataKeyPath xxx/keys/dataKey \
   --inputPath xxx/dbgen \
   --outputPath xxx/dbgen-encrypted

After encryption, you may upload encrypted data to Azure Data Lake store.

The example script is like below:

az storage fs directory upload -f myFS --account-name myDataLakeAccount -s xxx/dbgen-encrypted -d myDirectory --recursive

4.4 Running

Make sure you set the INPUT_DIR and OUTPUT_DIR in TpchQuery class before compiling to point to the location the of the input data and where the output should be saved.

The example script to run a query is like:

secure_password=`az keyvault secret show --name "key-pass" --vault-name $KEY_VAULT_NAME --query "value" | sed -e 's/^"//' -e 's/"$//'`

DATA_LAKE_NAME=
DATA_LAKE_ACCESS_KEY=
KEY_VAULT_NAME=
PRIMARY_KEY_PATH=
DATA_KEY_PATH=

LOCAL_IP=
RUNTIME_SPARK_MASTER=
INPUT_DIR=xxx/dbgen-encrypted
OUTPUT_DIR=xxx/output

export TF_MKL_ALLOC_MAX_BYTES=10737418240 && \
  /opt/jdk8/bin/java \
    -cp '/ppml/trusted-big-data-ml/work/bigdl-2.1.0-SNAPSHOT/lib/bigdl-ppml-spark_3.1.2-2.1.0-SNAPSHOT-jar-with-dependencies.jar:/ppml/trusted-big-data-ml/work/spark-3.1.2/conf/:/ppml/trusted-big-data-ml/work/spark-3.1.2/jars/*' \
    -Xmx10g \
    -Dbigdl.mklNumThreads=1 \
    org.apache.spark.deploy.SparkSubmit \
    --master $RUNTIME_SPARK_MASTER \
    --deploy-mode client \
    --name spark-tpch-sgx \
	--conf spark.driver.host=$LOCAL_IP
    --conf spark.driver.memory=18g \
    --conf spark.driver.cores=2 \
    --conf spark.executor.cores=2 \
    --conf spark.executor.memory=24g \
    --conf spark.executor.instances=2 \
    --conf spark.driver.defaultJavaOptions="-Dlog4j.configuration=/ppml/trusted-big-data-ml/work/spark-3.1.2/conf/log4j2.xml" \
    --conf spark.executor.defaultJavaOptions="-Dlog4j.configuration=/ppml/trusted-big-data-ml/work/spark-3.1.2/conf/log4j2.xml" \
    --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
    --conf spark.kubernetes.container.image=intelanalytics/bigdl-ppml-trusted-big-data-ml-python-graphene:2.1.1-SNAPSHOT \
    --conf spark.kubernetes.driver.podTemplateFile=/ppml/trusted-big-data-ml/spark-driver-template-kv.yaml \
    --conf spark.kubernetes.executor.podTemplateFile=/ppml/trusted-big-data-ml/spark-executor-template-kv.yaml \
    --conf spark.kubernetes.executor.deleteOnTermination=false \
    --conf spark.network.timeout=10000000 \
    --conf spark.executor.heartbeatInterval=10000000 \
    --conf spark.python.use.daemon=false \
    --conf spark.python.worker.reuse=false \
    --conf spark.sql.auto.repartition=true \
    --conf spark.default.parallelism=400 \
    --conf spark.sql.shuffle.partitions=400 \
    --jars local://$SPARK_EXTRA_JAR_PATH \
    --conf spark.kubernetes.sgx.enabled=true \
    --conf spark.kubernetes.sgx.driver.mem=16g \
    --conf spark.kubernetes.sgx.driver.jvm.mem=7g \
    --conf spark.kubernetes.sgx.executor.mem=16g \
    --conf spark.kubernetes.sgx.executor.jvm.mem=7g \
    --conf spark.kubernetes.sgx.log.level=error \
    --conf spark.authenticate=true \
    --conf spark.authenticate.secret=$secure_password \
    --conf spark.kubernetes.executor.secretKeyRef.SPARK_AUTHENTICATE_SECRET="spark-secret:secret" \
    --conf spark.kubernetes.driver.secretKeyRef.SPARK_AUTHENTICATE_SECRET="spark-secret:secret" \
    --conf spark.authenticate.enableSaslEncryption=true \
    --conf spark.network.crypto.enabled=true \
    --conf spark.network.crypto.keyLength=128 \
    --conf spark.network.crypto.keyFactoryAlgorithm=PBKDF2WithHmacSHA1 \
    --conf spark.io.encryption.enabled=true \
    --conf spark.io.encryption.keySizeBits=128 \
    --conf spark.io.encryption.keygen.algorithm=HmacSHA1 \
    --conf spark.ssl.enabled=true \
    --conf spark.ssl.port=8043 \
    --conf spark.ssl.keyPassword=$secure_password \
    --conf spark.ssl.keyStore=/ppml/trusted-big-data-ml/work/keys/keystore.jks \
    --conf spark.ssl.keyStorePassword=$secure_password \
    --conf spark.ssl.keyStoreType=JKS \
    --conf spark.ssl.trustStore=/ppml/trusted-big-data-ml/work/keys/keystore.jks \
    --conf spark.ssl.trustStorePassword=$secure_password \
    --conf spark.ssl.trustStoreType=JKS \
    --conf spark.hadoop.fs.azure.account.auth.type.${DATA_LAKE_NAME}.dfs.core.windows.net=SharedKey \
    --conf spark.hadoop.fs.azure.account.key.${DATA_LAKE_NAME}.dfs.core.windows.net=${DATA_LAKE_ACCESS_KEY} \
    --conf spark.hadoop.fs.azure.enable.append.support=true \
    --conf spark.bigdl.kms.type=AzureKeyManagementService \
    --conf spark.bigdl.kms.azure.vault=$KEY_VAULT_NAME \
    --conf spark.bigdl.kms.key.primary=$PRIMARY_KEY_PATH \
    --conf spark.bigdl.kms.key.data=$DATA_KEY_PATH \
    --class $SPARK_JOB_MAIN_CLASS \
    --verbose \
    /ppml/trusted-big-data-ml/work/bigdl-2.1.0-SNAPSHOT/lib/bigdl-ppml-spark_3.1.2-2.1.0-SNAPSHOT-jar-with-dependencies.jar \
    $INPUT_DIR $OUTPUT_DIR aes_cbc_pkcs5padding plain_text [QUERY]

INPUT_DIR is the tpch’s data dir. OUTPUT_DIR is the dir to write the query result. The optional parameter [QUERY] is the number of the query to run e.g 1, 2, …, 22