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- an extractor is installed as a service on a VM, so when a VM starts, all the extractors that the VM contains as services will start automatically and successfully; if services are do not fulfill all requirements, we might have to look into alternatives;
- the resource limitation of using extractors to process input data is CPU processing, not memory, disk I/O, or network I/O, so the design is only for scaling for CPU usage;
- the system needs to support multiple OS types, including both Linux and Windows;
- the system uses RabbitMQ as the messaging technology.
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- RabbitMQ queue lengths and the number of consumers for the queues;
Can be obtained using RabbitMQ management API. The number of consumers can be used to verify that the action to scale up/down succeeded. - for each queue, the corresponding extractor name;
Currently hard coded in the extractor code, so that queue name == extractor name. - for a given extractor, the list of running VMs where an instance of the extractor is running, and the list of suspended VMs where it was running;
Running VM list: can be obtained using RabbitMQ management API, queue --> connections --> IP.
Suspended VM list: when suspending a VM, update the mapping for the given extractor, remove the entry from the running VM list and add it to the suspended VM list.
Also maintain the data of mapping from a running/suspended VM to the extractors that it contains. This is useful in the scaling up part. - the number of vCPUs of the VMs;
This info is fixed for a given OpenStack flavor. The flavor must be specified when starting a VM, and this data can be stored at that time. - the load averages of the VMs;
For Linux, can be obtained by executing a command ("uptime" or "cat /proc/loadavg") with ssh. Learned a way to get it quickly in <1 second, usually 0.3 second. But if needed, can use a separate thread to get this data, instead of in-line in the execution flow. - for a given extractor type, the list of VM images where the extractor is available, and the entire command line (including arguments) to start another extractor instance, i.e., a pair of (VM image name, entire command line). The command line is needed only for running additional extractor instances, since the first instance of that extractor will be started automatically as a service.
Also maintain the data of mapping from a given VM image to the extractors it contains. This is useful in the scaling up part.
This is manual and static data. Can be stored in a config file, a MongoDB collection, or using other ways. - the last times a request is processed by the VMs.
Can be obtained using the RabbitMQ management API, /api/channels/: "idle_since" and "peer_host". Need to aggregate the channels that have the same peer_host IP, and skip the ones on the localhost. This info is used in the scaling down part.
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- If the threshold is reached for a given queue, say, q1, then use the data item 2 above, find the corresponding extractor (e1). Currently this is hardcoded in the extractors, so that queue name == extractor name. If the number of consumers / extractors for this queue is 0, find e1, and go straight to step 5 4 below to start a VM that contains e1.
- Look up e1 to find the corresponding running VM list, say, (vm1, vm2, vm3).
- Go through the list one by one. If there's an open slot in the VM, meaning its #vCPUs > loadavg + <cpu_buffer_room> (configurable, such as 0.5), for example, vm1 #vCPUs == 2, loadavg = 1.2, then start another instance of e1 on vm1. Return. If there's no open slot on vm1, look at the next VM in the list. Return if an open slot is found and another instance of e1 is started.
- If we go through the entire list and there's no open slot, or the list is empty, then look up e1 to find the corresponding suspended VM list, say, (vm4, vm5). If the list is not empty, resume the first VM in the list. After this, look up and find the other extractors running in the VM, and set a mark for them so that this scaling logic will skip these other extractors, as resuming this VM would also resume them. Return.
- If the above suspended VM list is empty, then we need to start a new VM to have more e1 instances. Look up e1 to find a VM image that contains it. Start a new VM using that image. Similar to the above step, after this, look up and find the other extractors available in the VM, and set a mark for them so that this scaling logic will skip these other extractors, as starting this VM would also resume them.
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The team seems to be familiar, more comfortable with Java, Scala or ScalaPython, so we consider using these. This module mainly operates by itself, instead of mainly serving client requests, so it does not have to be a web app. A stand-alone app seems a better fit.
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