TPC-H with Trusted SparkSQL on Kubernetes

Prerequisites

  • 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. 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.

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)

The example code of encrypt data with SimpleKeyManagementService 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 \
   --inputPath xxx/dbgen \
   --outputPath xxx/dbgen-encrypted

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=/root/keys/enclave-key.pem
export DATA_PATH=/root/zoo-tutorials/tpch-spark
export KEYS_PATH=/root/keys
export KUBERCONFIG_PATH=/root/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 $ENCLAVE_KEY:/graphene/Pal/src/host/Linux-SGX/signer/enclave-key.pem \
        -v $DATA_PATH:/ppml/trusted-big-data-ml/work/tpch-spark \
        -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_K8S_SPARK_IMAGE=$DOCKER_IMAGE \
        -e RUNTIME_DRIVER_HOST=$LOCAL_IP \
        -e RUNTIME_DRIVER_PORT=54321 \
        -e RUNTIME_EXECUTOR_INSTANCES=1 \
        -e RUNTIME_EXECUTOR_CORES=4 \
        -e RUNTIME_EXECUTOR_MEMORY=20g \
        -e RUNTIME_TOTAL_EXECUTOR_CORES=4 \
        -e RUNTIME_DRIVER_CORES=4 \
        -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
spec:
  containers:
  - name: spark-executor
    securityContext:
      privileged: true
    volumeMounts:
	...
      - name: tpch
        mountPath: /ppml/trusted-big-data-ml/work/tpch-spark
      - name: kubeconf
        mountPath: /root/.kube/config
  volumes:
    - name: enclave-key
      hostPath:
        path:  /root/keys/enclave-key.pem
	...
    - name: tpch
      hostPath:
        path: /path/to/tpch-spark
    - name: kubeconf
      hostPath:
        path: /path/to/kuberconfig

  1. Run PPML TPC-H bash``` 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 SPARK_LOCAL_IP=$LOCAL_IP && export INPUT_DIR=xxx/dbgen export OUTPUT_DIR=xxx/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 $RUNTIME_SPARK_MASTER –deploy-mode client –name spark-tpch-sgx –conf spark.driver.host=$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 spark.network.timeout=10000000 –conf spark.starvation.timeout=250000 –conf spark.rpc.askTimeout=600 –conf spark.sql.autoBroadcastJoinThreshold=-1 –conf spark.io.compression.codec=lz4 –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=$secure_password –conf spark.kubernetes.executor.secretKeyRef.SPARK_AUTHENTICATE_SECRET=”spark-secret:secret” –conf spark.kubernetes.driver.secretKeyRef.SPARK_AUTHENTICATE_SECRET=”spark-secret:secret” –conf spark.authenticate.enableSaslEncryption=true –conf spark.network.crypto.enabled=true –conf spark.network.crypto.keyLength=128 –conf spark.network.crypto.keyFactoryAlgorithm=PBKDF2WithHmacSHA1 –conf spark.io.encryption.enabled=true –conf spark.io.encryption.keySizeBits=128 –conf spark.io.encryption.keygen.algorithm=HmacSHA1 –conf spark.ssl.enabled=true –conf spark.ssl.port=8043 –conf spark.ssl.keyPassword=$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.bigdl.kms.type=SimpleKeyManagementService –conf spark.bigdl.kms.simple.id=simpleAPPID –conf spark.bigdl.kms.simple.key=simpleAPPKEY –conf spark.bigdl.kms.key.primary=xxxx/primaryKey –conf spark.bigdl.kms.key.data=xxxx/dataKey –class com.intel.analytics.bigdl.ppml.examples.tpch.TpchQuery –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]