We have developed a Flink application on v1.13.0 and deployed it on Kubernetes that runs a Task Manager instance on a Kubernetes pod. I am not sure how to determine the ideal number of task slots on each Task Manager instance. Should we configure/choose one task slot on each task manager/pod or two slots per Task Manager/pod or more. We currently configured two task slots per Task Manager instance and wondering if that is the right choice/setting. What are the pros and cons of running one task slot vs running two or more slots on a Task Manager/pod.
As a general rule, for containerized deployments (like yours), one slot per TM is a good default starting point. This tends to keep the configuration as straightforward as possible.
Depends on your expected workload, input, state size.
Is it a batch or a stream?
Batch: is the worload fast enough?
Stream: is the worload backpressuring?
For these throughput limitations, you might want to increase the number of TMs
State size: how are you processing your data? Does it require a lot of state?
For example, this query:
SELECT
user_id,
count(*)
FROM user_logins
will need a state proportional with the number of users.
You can tune the memory of TM in the options.
Here is a useful link: https://www.ververica.com/blog/how-to-size-your-apache-flink-cluster-general-guidelines
Concurrent jobs: is this machine under-used, and do you need to keep a pool of unused TS ready to execute a job?
A TM's memory will be sliced between the TS (be sure it fits your state size), but the CPU will be shared when idle.
Other than that if it's going fine on one TM on one pod then you have nothing to do.
Related
I have 5 different jobs running in 5 task slots. They all read from Kafka and sink back to Kafka. Kafka load is about 200K messages/sec.
I have another job, lets say ,job6 which needs to get some information from these 5 jobs. For each device we make some calculations in those 5 jobs, and according the results of this calculations, in the 6. task I need to do something more.
As a first solution, I used sideOutputs in these 5 jobs and sent these additional info to an Kafka topic. Then my 6. job subscribed to it. But as the workload on Kafka was already very high, this solution doubled the workload on Kafka.
As all task slots run in the same task manager JVM, what I have in my mind is , developing custom RichSink and RichSource functions which use same static/singleton java object. As it will be static, I beleive all tasks will have access to same object. This object will keep a queue (java BlockingQueue).Instead of feeding data to Kafka, I will feed this queue in all tasks and 6.task will process the data received from this queue.
Please let me know if this is a good idea for a big distributed system. I assume clusters will not be a problem because after reading data from shared queue, I will call keyBy() so I hope Flink will handle that part. Also please let me know dangereous points and tips if you have.
You essentially have an in-memory data store for bridging between two jobs. One of several issues here is that if the Task Manager crashes, you lose this data, thus eliminating one of the key benefits of Flink (guaranteed at-least-once or exactly-once processing).
You'd also have to ensure that you've got at least one of your job 6 source operators running in a slot on every TM instance. Flink doesn't yet support the ability to easily control which sub-tasks run in what slots, though if you set the downstream job's parallelism == the number of slots then you can work around that issue.
I'm sure there are other issues, I just haven't spent much time thinking about it :)
Depending on the version of Flink you're using, I wonder if Flink's new Table Store would be an option for you.
The GlobalAggregateManager in the Flink may be helpful.
This can be used to share the state amongst parallel tasks in a job. However, performance may be poor in high-throughput scenarios.
Here are some demos of these projects:
Arctic, Flink
I had few questions in flink stream processing framework. Please let me know the your comments on these questions.
Let say If I build the cluster with n nodes, out of which I had m nodes as job mangers (for HA) then, remaining nodes (n-m) are the ask mangers?
In each node, We had n cores then how we can control/to use the specific number of cores to task-manger/job-manger?
If we add the new node as task-manger then, does the job manger automatically assign the task to the newly added task-manger?
Does flink has concept of partitions and data skew?
If flink connects to pulsar and need to read the data from portioned topic. So, what is the parallelism here? (parallelism is equal to no. of partitions or it's completely depends the flink task-manager's no.of task slots)
Does flink has any inbuilt optimization on job graph? (Example. My job graph has so many filter, map , flatmap.. etc). Please can you suggest any docs/materials for flink job optimizations?
do we have any option like, one dedicated core can be used for prometheus metrics scraping?
Yes
Configuring the number of slots per TM: https://nightlies.apache.org/flink/flink-docs-stable/docs/concepts/flink-architecture/#task-slots-and-resources although each operator runs in its own thread and you have no control on which core they run, so you don't really have a fine-grained control of how cores are used. Configuring resource groups also allows you to distribute operators across slots: https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/datastream/operators/overview/#task-chaining-and-resource-groups
Not for currently running jobs, you'd need to re-scale them. New jobs will use it though.
Yes. https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/datastream/sources/
It will depend on the Fink source parallelism.
It automatically optimizes the graph as it sees fit. You have some control rescaling and chaining/splitting operators: https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/datastream/operators/overview/ (towards the end). As a rule of thumb, I would start deploying a full job per slot and then, once properly understood where are the bottlenecks, try to optimize the graph. Most of the time is not worth it due to increased serialization and shuffling of data.
You can export Prometheus metrics, but not have a core dedicated to it: https://nightlies.apache.org/flink/flink-docs-stable/docs/deployment/metric_reporters/#prometheus
According to the Flink documentation, there exist two dimensions to affect the amount of resources available to a task:
The number of task managers
The number of task slots available to a task manager.
Having one slot per TaskManager means each task group runs in a separate JVM (which can be started in a separate container, for example). Having multiple slots means more subtasks share the same JVM. Tasks in the same JVM share TCP connections (via multiplexing) and heartbeat messages. They may also share data sets and data structures, thus reducing the per-task overhead.
With this line in the documentation, it seems that you would always err on the side of increasing the number of task slots per task manager instead of increasing the number of task managers.
A concrete scenario: if I have a job cluster deployed in Kubernetes (let's assume 16 CPU cores are available) and a pipeline consisting of one source + one map function + one sink, then I would default to having a single TaskManager with 16 slots available to that TaskManager.
Is this the optimal configuration? Is there a case where I would prefer 16 TaskManagers with a single slot each or maybe a combination of TaskManager and slots that could take advantage of all 16 CPU cores?
There is no optimal configuration because "optimal" cannot be defined in general. A configuration with a single slot per TM provides good isolation and is often easier to manage and reason about.
If you run multiple jobs, a multi-slot configuration might schedule tasks of different jobs to one TM. If the TM goes down, e.g., because either of two tasks consumed too much memory, both jobs will be restarted. On the other hand, running one slot per TM might leave more memory unused. If you only run a single job per cluster, multiple slots per TM might be fine.
I'm prototyping a Flink streaming application on a bare-metal cluster of 15 machines. I'm using yarn-mode with 90 task slots (15x6).
The app reads data from a single Kafka topic. The Kafka topic has 15 partitions, so I set the parallelism of the source operator to 15 as well. However, I found that Flink in some cases assigns 2-4 instances of the consumer task to the same taskmanager. This causes certain nodes to become network-bound (the Kafka topic is serving high volume of data and the machines only have 1G NICs) and bottlenecks in the entire data flow.
Is there a way to "force" or otherwise instruct Flink to distribute a task evenly across all taskmanagers, perhaps round robin? And if not, is there a way to manually assign tasks to specific taskmanager slots?
To the best of my knowledge, this isn't possible. The job manager, which schedules tasks into task slots, is only aware of task slots. It isn't aware that some task slots belong to one task manager, and others to another task manager.
Flink does not allow manually assign task slots as in case of failure handling, it can distribute the task to remaining task managers.
However, you can distribute the workload evenly by setting cluster.evenly-spread-out-slots: true in flink-conf.yaml.
This works for Flink >= 1.9.2.
To make it work, you may also have to set:
taskmanager.numberOfTaskSlots equal to the number of available CPUs per machine, and
parallelism.default equal to the the total number of CPUs in the cluster.
Consider a Flink cluster with some nodes where each node has a multi-core processor. If we configure the number of the slots based on the number of cores and equal share of memory, how does Apache Flink distribute the tasks between the nodes and the free slots? Are they fairly treated?
Is there any way to make/configure Flink to treat the slots equally when we configure the task slots based on the number of the cores available on a node
For instance, assume that we partition the data equally and run the same task over the partitions. Flink uses all the slots from some nodes and at the same time some nodes are totally free. The node which has less number of CPU cores involved outputs the result much faster than the node with more number of CPU cores involved in the process. Apart from that, this ratio of speedup is not proportional to the number of used cores in each node. In other words, if in one node one core is occupied and in another node two cores are occupied, in fairly treating each core as a slot, each slot should output the result over the same task in almost equal amount of time irrespective of which node they belong to. But, this is not the case here.
With this assumption, I would say that the nodes are not treated equally. This in turn produces a result time wise that is not proportional to the number of the nodes available. We can not say that increasing the number of the slots necessarily decreases the time cost.
I would appreciate any comment from the Apache Flink Community!!
Flink's default strategy as of version >= 1.5 considers every slot to be resource-wise the same. With this assumption, it should not matter wrt resources where you place the tasks since all slots should be the same. Given this, the main objective for placing tasks is to colocate them with their inputs in order to minimize network I/O.
If we are now in a standalone setup where we have a fixed number of TaskManagers running, Flink will pick slots in an arbitrary fashion (no guarantee given) for the sources and then colocate their consumers in the same slots if possible.
When running Flink on Yarn or Mesos where Flink can start new TaskManagers, Flink will first use up all slots of an existing TaskManager before it requests a new one. In this case, you will see that all sources will end up on as few TaskManagers as possible.
Since CPUs are not isolated wrt slots (they are a shared resource), the above-mentioned assumption does not hold true in all cases. Hence, in some cases where you have a fixed set of TaskManagers it is actually beneficial to spread the tasks out as much as possible to make use of the shared CPU resources.
In order to support this kind of scheduling strategy, the Flink community added the task spread out strategy via FLINK-12122. In order to use a scheduling strategy which is more similar to the pre FLIP-6 behaviour where Flink tries to spread out the workload across all available TaskExecutors, one needs to set cluster.evenly-spread-out-slots: true in the flink-conf.yaml
Very old thread, but there is a newer thread that answers this question for current versions.
with Flink 1.5 we added resource elasticity. This means that Flink is now able to allocate new containers on a cluster management framework like Yarn or Mesos. Due to these changes (which also apply to the standalone mode), Flink no longer reasons about a fixed set of TaskManagers because if needed it will start new containers (does not work in standalone mode). Therefore, it is hard for the system to make any decisions about spreading slots belonging to a single job out across multiple TMs. It gets even harder when you consider that some jobs like yours might benefit from such a strategy whereas others would benefit from co-locating its slots. It gets even more complicated if you want to do scheduling wrt to multiple jobs which the system does not have full knowledge about because they are submitted sequentially. Therefore, Flink currently assumes that slots requests can be fulfilled by any TaskManager.