TPC-H with Trusted SparkSQL on Kubernetes


  • Hardware that supports SGX

  • A fully configured Kubernetes cluster

  • Intel SGX Device Plugin to use SGX in K8S cluster (install following instructions here)

Prepare TPC-H kit and data

  1. Generate data

Go to TPC Download site, choose TPC-H source code, then download the TPC-H toolkits. Follow the download instructions carefully. After you download the tpc-h tools zip and uncompressed the zip file. Go to dbgen directory, and create makefile based on makefile.suite, and modify makefile according to the prompts inside, 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:


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.

You need to move all .tbl files to a new directory as raw data.

You can then either upload your data to remote file system or read them locally.

  1. Encrypt Data

Encrypt data with specified Key Management Service (SimpleKeyManagementService, or EHSMKeyManagementService , or AzureKeyManagementService). Details can be found here:

The example code of encrypt data with SimpleKeyManagementService is like below:

java -cp "$BIGDL_HOME/jars/bigdl-ppml-spark_3.1.2-2.1.0-SNAPSHOT.jar:$SPARK_HOME/conf/:$SPARK_HOME/jars/*:$BIGDL_HOME/jars/*"  \
   -Xmx10g \ \
   --inputPath xxx/dbgen-input \
   --outputPath xxx/dbgen-encrypted
   --kmsType SimpleKeyManagementService
   --simpleAPPID xxxxxxxxxxxx \
   --simpleAPPKEY xxxxxxxxxxxx \
   --primaryKeyPath /path/to/simple_encrypted_primary_key \
   --dataKeyPath /path/to/simple_encrypted_data_key

Deploy PPML TPC-H on Kubernetes

  1. Pull docker image

sudo docker pull intelanalytics/bigdl-ppml-trusted-big-data-ml-python-graphene:2.1.0-SNAPSHOT
  1. Prepare SGX keys (following instructions here), make sure keys and tpch-spark can be accessed on each K8S node

  2. Start a bigdl-ppml enabled Spark K8S client container with configured local IP, key, tpch and kuberconfig path

export ENCLAVE_KEY=/path/to/enclave-key.pem
export SECURE_PASSWORD_PATH=/path/to/password
export DATA_PATH=/path/to/data
export KEYS_PATH=/path/to/keys
export KUBERCONFIG_PATH=/path/to/kuberconfig
export LOCAL_IP=$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 \
        --name=spark-local-k8s-client \
        --oom-kill-disable \
        --device=/dev/sgx/enclave \
        --device=/dev/sgx/provision \
        -v /var/run/aesmd/aesm.socket:/var/run/aesmd/aesm.socket \
        -v $SECURE_PASSWORD_PATH:/ppml/trusted-big-data-ml/work/password \
        -v $ENCLAVE_KEY:/graphene/Pal/src/host/Linux-SGX/signer/enclave-key.pem \
        -v $DATA_PATH:/ppml/trusted-big-data-ml/work/data \
        -v $KEYS_PATH:/ppml/trusted-big-data-ml/work/keys \
        -v $KUBERCONFIG_PATH:/root/.kube/config \
        -e RUNTIME_SPARK_MASTER=k8s://https://$LOCAL_IP:6443 \
        -e RUNTIME_K8S_SERVICE_ACCOUNT=spark \
        -e RUNTIME_DRIVER_PORT=54321 \
        -e RUNTIME_DRIVER_MEMORY=10g \
        -e SGX_MEM_SIZE=64G \
        -e SGX_LOG_LEVEL=error \
        -e LOCAL_IP=$LOCAL_IP \
        $DOCKER_IMAGE bash
  1. Attach to the client container

sudo docker exec -it spark-local-k8s-client bash
  1. Modify spark-executor-template.yaml, add path of enclave-key, tpch-spark and kuberconfig on host

apiVersion: v1
kind: Pod
  - name: spark-executor
      privileged: true
      - name: tpch
        mountPath: /ppml/trusted-big-data-ml/work/tpch-spark
      - name: kubeconf
        mountPath: /root/.kube/config
    - name: enclave-key
        path:  /root/keys/enclave-key.pem
    - name: tpch
        path: /path/to/tpch-spark
    - name: kubeconf
        path: /path/to/kuberconfig
  1. Run PPML TPC-H

secure_password=`openssl rsautl -inkey /ppml/trusted-big-data-ml/work/password/key.txt -decrypt </ppml/trusted-big-data-ml/work/password/output.bin` && \
export TF_MKL_ALLOC_MAX_BYTES=10737418240 && \
export INPUT_DIR=xxx/dbgen-encrypted && \
export OUTPUT_DIR=xxx/dbgen-output && \
  /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 $RUNTIME_SPARK_MASTER \
    --deploy-mode client \
    --name spark-tpch-sgx \
    --conf$LOCAL_IP \
    --conf spark.driver.port=54321 \
    --conf spark.driver.memory=10g \
    --conf spark.driver.blockManager.port=10026 \
    --conf spark.blockManager.port=10025 \
    --conf spark.scheduler.maxRegisteredResourcesWaitingTime=5000000 \
    --conf spark.worker.timeout=600 \
    --conf spark.python.use.daemon=false \
    --conf spark.python.worker.reuse=false \
    --conf \
    --conf spark.starvation.timeout=250000 \
    --conf spark.rpc.askTimeout=600 \
    --conf spark.sql.autoBroadcastJoinThreshold=-1 \
    --conf \
    --conf spark.sql.shuffle.partitions=8 \
    --conf spark.speculation=false \
    --conf spark.executor.heartbeatInterval=10000000 \
    --conf spark.executor.instances=24 \
    --executor-cores 8 \
    --total-executor-cores 192 \
    --executor-memory 16G \
    --properties-file /ppml/trusted-big-data-ml/work/bigdl-2.1.0-SNAPSHOT/conf/spark-bigdl.conf \
    --conf spark.kubernetes.authenticate.serviceAccountName=spark \
    --conf spark.kubernetes.container.image=$RUNTIME_K8S_SPARK_IMAGE \
    --conf spark.kubernetes.executor.podTemplateFile=/ppml/trusted-big-data-ml/spark-executor-template.yaml \
    --conf spark.kubernetes.executor.deleteOnTermination=false \
    --conf spark.kubernetes.executor.podNamePrefix=spark-tpch-sgx \
    --conf spark.kubernetes.sgx.enabled=true \
    --conf spark.kubernetes.sgx.executor.mem=32g \
    --conf spark.kubernetes.sgx.executor.jvm.mem=10g \
    --conf spark.kubernetes.sgx.log.level=$SGX_LOG_LEVEL \
    --conf spark.authenticate=true \
    --conf spark.authenticate.secret=$secure_password \
    --conf spark.kubernetes.executor.secretKeyRef.SPARK_AUTHENTICATE_SECRET="spark-secret:secret" \
    --conf spark.kubernetes.driver.secretKeyRef.SPARK_AUTHENTICATE_SECRET="spark-secret:secret" \
    --conf spark.authenticate.enableSaslEncryption=true \
    --conf \
    --conf \
    --conf \
    --conf \
    --conf \
    --conf \
    --conf spark.ssl.enabled=true \
    --conf spark.ssl.port=8043 \
    --conf spark.ssl.keyPassword=$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.bigdl.kms.type=SimpleKeyManagementService \
    --conf \
    --conf spark.bigdl.kms.simple.key=simpleAPIKEY \
    --conf spark.bigdl.kms.key.primary=xxxx/primaryKey \
    --conf \
    --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]

The optional parameter [QUERY] is the number of the query to run e.g 1, 2, …, 22.

The result is in OUTPUT_DIR. There should be a file called TIMES.TXT with content formatted like:

Q01 39.80204010