Support multiple lightweight components of same type

[maany]

Describe the bug
Input:

global_variables:
  - &spark_hadoop_master_ip_address 188.184.95.231
  - &spark_hadoop_master_fqdn spark-master.cern.ch
  - &spark_hadoop_worker_ip_address 188.184.104.92
  - &spark_hadoop_worker_fqdn spark-worker.cern.ch
  - &spark_hadoop_add_worker_1_ip_address 188.184.31.131
  - &spark_hadoop_add_worker_1_fqdn spark-add-worker-1.cern.ch
  - &spark_hadoop_add_worker_2_ip_address 188.184.30.108
  - &spark_hadoop_add_worker_2_fqdn spark-add-worker-2.cern.ch
  - &spark_hadoop_add_worker_3_ip_address 188.184.28.84
  - &spark_hadoop_add_worker_3_fqdn spark-add-worker-3.cern.ch
  - &spark_hadoop_add_worker_4_ip_address 188.185.82.126
  - &spark_hadoop_add_worker_4_fqdn spark-submit.cern.ch

spark_worker_runtime_variables:
  - &spark_hadoop_worker_runtime_var_spark_hadoop_master_fqdn
    __from__: *spark_hadoop_master_fqdn

preferred_tech_stack:
  level_1_configuration: puppet
  level_2_configuration: sh
  container_orchestration: docker-swarm
  container: docker

site_infrastructure:
  - fqdn: *spark_hadoop_master_fqdn
    ip_address: *spark_hadoop_master_ip_address
  - fqdn: *spark_hadoop_worker_fqdn
    ip_address: *spark_hadoop_worker_ip_address
  - fqdn: *spark_hadoop_add_worker_1_fqdn
    ip_address: *spark_hadoop_add_worker_1_ip_address
  - fqdn: *spark_hadoop_add_worker_2_fqdn
    ip_address: *spark_hadoop_add_worker_2_ip_address
  - fqdn: *spark_hadoop_add_worker_3_fqdn
    ip_address: *spark_hadoop_add_worker_3_ip_address
  - fqdn: *spark_hadoop_add_worker_4_fqdn
    ip_address: *spark_hadoop_add_worker_4_ip_address

lightweight_components:
  - name: spark-hadoop-master
    type: spark_hadoop_master
    repository_url: "https://github.com/maany/simple_spark_cluster_master"
    repository_revision: "master"
    execution_id: 5
    deploy:
      - node: *spark_hadoop_master_fqdn
        container_count: 1
    config:
        enable_init_daemon: false
        spark_driver_memory: "1g"
        spark_executor_memory: "1g"
        spark_yarn_am_memory: "512m"
        spark_history_fs_log_directory: "hdfs://spark-master.cern.ch:9000/spark-logs"
        spark_history_fs_update_interval: "30s"

  - name: spark-hadoop-worker
    type: spark_hadoop_worker
    repository_url: "https://github.com/maany/simple_spark_cluster_worker"
    repository_revision: "master"
    execution_id: 0
    deploy:
      - node: *spark_hadoop_worker_fqdn
        container_count: 1
    config:
        enable_init_daemon: false
        spark_master: *spark_hadoop_master_fqdn
        spark_driver_memory: "1g"
        spark_executor_memory: "1g"
        spark_yarn_am_memory: "512m"
        spark_history_fs_log_directory: "hdfs://spark-master.cern.ch:9000/spark-logs"

  - name: spark-hadoop-worker
    type: spark_hadoop_worker
    repository_url: "https://github.com/maany/simple_spark_cluster_worker"
    repository_revision: "master"
    execution_id: 1
    deploy:
      - node: *spark_hadoop_add_worker_1_fqdn
        container_count: 1
    config:
        enable_init_daemon: false
        spark_master: *spark_hadoop_master_fqdn
        spark_driver_memory: "1g"
        spark_executor_memory: "1g"
        spark_yarn_am_memory: "512m"
        spark_history_fs_log_directory: "hdfs://spark-master.cern.ch:9000/spark-logs"

  - name: spark-hadoop-worker
    type: spark_hadoop_worker
    repository_url: "https://github.com/maany/simple_spark_cluster_worker"
    repository_revision: "master"
    execution_id: 2
    deploy:
      - node: *spark_hadoop_add_worker_2_fqdn
        container_count: 1
    config:
        enable_init_daemon: false
        spark_master: *spark_hadoop_master_fqdn
        spark_driver_memory: "1g"
        spark_executor_memory: "1g"
        spark_yarn_am_memory: "512m"
        spark_history_fs_log_directory: "hdfs://spark-master.cern.ch:9000/spark-logs"

  - name: spark-hadoop-worker
    type: spark_hadoop_worker
    repository_url: "https://github.com/maany/simple_spark_cluster_worker"
    repository_revision: "master"
    execution_id: 3
    deploy:
      - node: *spark_hadoop_add_worker_3_fqdn
        container_count: 1
    config:
        enable_init_daemon: false
        spark_master: *spark_hadoop_master_fqdn
        spark_driver_memory: "1g"
        spark_executor_memory: "1g"
        spark_yarn_am_memory: "512m"
        spark_history_fs_log_directory: "hdfs://spark-master.cern.ch:9000/spark-logs"

  - name: spark-hadoop-worker
    type: spark_hadoop_worker
    repository_url: "https://github.com/maany/simple_spark_cluster_worker"
    repository_revision: "master"
    execution_id: 4
    deploy:
      - node: *spark_hadoop_add_worker_4_fqdn
        container_count: 1
    config:
        enable_init_daemon: false
        spark_master: *spark_hadoop_master_fqdn
        spark_driver_memory: "1g"
        spark_executor_memory: "1g"
        spark_yarn_am_memory: "512m"
        spark_history_fs_log_directory: "hdfs://spark-master.cern.ch:9000/spark-logs"

What output/error did you get?
Error about duplicate meta_info_{worker} entries in runtime.yaml

To Reproduce
Run the compiler with the above input

Expected behavior
We should be able to create multiple entries for lightweight components of the same type. In case their config is different, this is required.

Logs
If applicable, attach logs from the yaml compiler

Desktop (please complete the following information):

• OS: All
• Python 2.7

Additional context
Add any other context about the problem here.