@Scout i panned to test performance of dagster how many able to trigger at the same time. mem ory consumption is there any easy way to do it

To test the performance of Dagster and determine how many jobs you can trigger simultaneously, as well as to monitor memory consumption, you can follow a systematic approach. Here are some steps you can take based on the documentation: 1. Benchmarking Concurrent Job Execution: - You can set up a test environment where you create a job with a known memory footprint. - Use the

QueuedRunCoordinator

to manage the execution of runs. You can configure the

dequeue_num_workers

setting to control how many runs are pulled from the run queue simultaneously. This can help you test how many jobs Dagster can handle at once. - The relevant configuration snippet from the documentation is as follows:

yaml
     run_coordinator:
       module: dagster.core.run_coordinator
       class: QueuedRunCoordinator
       config:
         dequeue_use_threads: true
         dequeue_num_workers: 8

- Adjust the

dequeue_num_workers

to the number of concurrent jobs you want to test. 2. Monitoring Memory Consumption: - You can use monitoring tools that are compatible with your deployment environment to track memory usage. For instance, if you are running Dagster on Kubernetes, you can use metrics-server or a monitoring solution like Prometheus and Grafana to monitor memory consumption. - Ensure that you have logging enabled for your jobs to capture any memory-related issues or errors that may occur during execution. 3. Limiting Concurrency: - If you want to limit the overall concurrency to prevent overwhelming your system, you can use concurrency limits. This can also be a way to test how the system behaves under controlled load. - The documentation provides guidance on limiting concurrency in data pipelines, which can be found [here](https://docs.dagster.io/guides/limiting-concurrency-in-data-pipelines#limiting-overall-concurrency-in-a-job). 4. Scaling Tests: - Start with a small number of concurrent jobs and gradually increase the load while monitoring system performance and memory usage. - Observe how the system behaves under different loads and identify any bottlenecks or performance degradation. 5. Reviewing System Logs and Metrics: - After running your tests, review the logs and metrics collected to analyze the performance and memory consumption. - Look for patterns or specific points where performance issues arise. Remember to conduct these tests in a controlled environment that does not impact your production systems. Testing in an isolated environment will allow you to push the limits of the system without risking downtime or data loss. Please note that the specifics of how to set up and run these tests will depend on your particular infrastructure and the complexity of your Dagster jobs. T

@Scout how to know at the point how much op is running. is ther anyway to get using api or directly from database

To know how many ops are currently running, you can use the GraphQL API provided by Dagster to query the status of runs and steps (ops within runs). Dagster's GraphQL API is a powerful tool for introspecting the state of your Dagster instance, including the status of runs and the individual ops within those runs. Here's a general approach to using the GraphQL API to determine the number of running ops: 1. Set up GraphQL Queries: You will need to write GraphQL queries that can fetch the status of runs and their steps. You can use the

runs

query to fetch runs and filter them by status (e.g.,

IN_PROGRESS

). Then, for each run, you can query the steps and their status. 2. Use the Dagit Playground: Dagit, the web interface for Dagster, includes an interactive GraphQL playground where you can develop and test your queries. 3. Automate the Queries: Once you have the queries set up, you can automate them using a script or a tool that can execute GraphQL queries against the Dagster GraphQL endpoint. 4. Parse the Response: The response from the GraphQL API will need to be parsed to count the number of ops that are currently running. Here is an example of a GraphQL query that you might use to fetch the status of runs and their steps:

graphql {   runs(filter: {statuses: [STARTED]}) {     results {       runId       status       stepStats {         stepKey         status       }     }   } }

This query fetches all runs that have a status of

STARTED

and retrieves the

runId

, overall

status

of the run, and the

status

of each step within the run. You would then need to iterate over the results and count the number of steps that have a status indicating they are currently running (e.g.,

IN_PROGRESS

). Please note that the exact field names and query structure may vary depending on the version of Dagster you are using, so you should refer to the [Dagster GraphQL API documentation](https://docs.dagster.io/concepts/dagit/graphql) for the most accurate and up-to-date information. If you prefer to query the database directly, you would need to understand the schema of the Dagster instance's underlying database. However, directly querying the database is not recommended because the schema could change between versions, and it is not part of the official API contract. Using the GraphQL API is the supported and recommended method for querying Dagster's state.

@Scout what is the maximum concurrency able to set?

In Dagster, the maximum concurrency—that is, the maximum number of runs that are allowed to be in progress at once—is configurable and can be set according to the resources available and the requirements of your deployment. By default, the

QueuedRunCoordinator

has a default limit of 10 concurrent runs, but you can adjust this limit to meet your needs. Here's the relevant part of the configuration for the `QueuedRunCoordinator`:

yaml run_coordinator:   module: dagster.core.run_coordinator   class: QueuedRunCoordinator   config:     max_concurrent_runs: <desired_limit>

In the

<desired_limit>

placeholder, you can specify the maximum number of concurrent runs you want to allow. If you want to remove the limit entirely, you can set this value to

-1

, which means there is no limit on the number of concurrent runs. However, it's important to be cautious when setting this value to

-1

, as it could potentially overwhelm your system's resources. Keep in mind that the appropriate limit for your use case will depend on various factors, including the complexity of your jobs, the resources of your execution environment, and the performance characteristics of the underlying infrastructure (e.g., database, compute resources). It's also worth noting that you can set concurrency limits at a more granular level using tags, which allows you to apply different limits to runs with specific tags. This can be useful if you have certain jobs that require stricter concurrency controls due to external dependencies or resource constraints. Here's an example of how you might configure tag-based concurrency limits:

yaml run_coordinator:   module: dagster.core.run_coordinator   class: QueuedRunCoordinator   config:     tag_concurrency_limits:       - key: "resource"         value: "database"         limit: 5       - key: "resource"         value: "api"         limit: 3

In this example, runs with the tag

resource: database

are limited to 5 concurrent runs, while runs with the tag

resource: api

are limited to 3 concurrent runs. When configuring concurrency limits, it's essential to balance the need for parallelism with the need to avoid overloading your systems. It's often a good idea to start with conservative limits and then adjust them based on observed performance and resource utilization.

@Scout let say i allocated 5gb memory and 8 cpus how much can i set conncurrent run count what is the maximum value ?

The maximum number of concurrent runs you can set in Dagster when you have allocated 5GB of memory and 8 CPUs depends on several factors, including: 1. Resource Requirements of Individual Runs: How much memory and CPU does each run typically consume? If each run requires a lot of memory or CPU, you will be able to run fewer of them concurrently. 2. Overhead: There is always some overhead in running a system, so you cannot allocate all 5GB of memory and all 8 CPUs exclusively to the runs. The operating system and other processes will also need resources. 3. Variability in Workload: The resource usage of a run might not be constant; it may have peaks and troughs. You need to account for these variations to avoid overcommitting resources. 4. Other System Components: If there are other components of the system running on the same hardware (e.g., databases, web servers, other applications), they will also consume resources. Given these factors, there is no one-size-fits-all answer to the maximum number of concurrent runs you can set. It requires careful consideration and potentially some trial and error to find the right balance. Here are some steps you can take to determine an appropriate concurrency limit: 1. Benchmark Individual Runs: Determine the average and peak resource usage of individual runs. This will give you a baseline for how many runs you can expect to handle concurrently. 2. Start Conservatively: Begin with a conservative number of concurrent runs. For example, if you estimate that each run might consume up to 1GB of memory, you might start with a limit of 3-4 concurrent runs to avoid exceeding your 5GB total memory. 3. Monitor and Adjust: Use monitoring tools to observe the actual resource usage of your runs in production. Adjust the concurrency limit based on the observed data. 4. Consider Failures and Retries: Ensure that your system can handle failures and retries without running out of resources. This might mean setting a lower concurrency limit to leave headroom for unexpected spikes in resource usage. 5. Use Dagster's Configuration: Set the

max_concurrent_runs

in your Dagster configuration based on your findings. Remember that you can always adjust this setting as you gather more data about your runs' performance. Remember that the maximum concurrency limit is not just about what the hardware can handle; it's also about maintaining system stability and performance. It's better to err on the side of caution and increase the limit gradually while monitoring the impact on system performance.

how to benchmark this value?

Benchmarking the appropriate value for maximum concurrency in Dagster involves testing how your system performs under different loads and monitoring the resource usage. Here's a step-by-step guide on how to benchmark this value: 1. Create a Representative Workload: - Identify a typical job or set of jobs that you want to benchmark. - Ensure that the jobs are representative of the actual workload in terms of complexity, data volume, and resource usage. 2. Set Up Monitoring: - Implement monitoring for CPU, memory, disk I/O, and network usage. Tools like Prometheus, Grafana, or cloud provider-specific tools (e.g., AWS CloudWatch, Google Cloud Monitoring) can be used for this purpose. - Ensure you can track the start, duration, and end of each job run, as well as any errors or failures. 3. Start with a Single Run: - Execute a single run of your job and monitor the resource usage. - Record the peak and average CPU and memory usage, as well as the execution time. 4. Incrementally Increase Concurrency: - Gradually increase the number of concurrent runs, starting with a low number (e.g., 2 concurrent runs). - After each increase, monitor the system's performance and resource usage. - Pay attention to how the resource usage scales with the number of concurrent runs. 5. Identify Bottlenecks: - Look for signs of resource contention or bottlenecks, such as increased execution time, memory swapping, or CPU saturation. - If you notice performance degradation, you may have reached the system's limits. 6. Determine the Optimal Concurrency Level: - The optimal concurrency level is the highest number of concurrent runs that your system can handle without significant performance degradation. - This level will be below the point where you start to see resource contention or failures. 7. Test Under Different Conditions: - Run the benchmark tests during different times of the day or week to account for variability in system load. - Consider running stress tests where you push the system beyond its normal limits to understand how it behaves under extreme conditions. 8. Document and Repeat: - Document your findings and the methodology used for benchmarking. - Repeat the benchmarking process periodically, especially after significant changes to your jobs or infrastructure. 9. Automate the Process: - If possible, automate the benchmarking process to regularly test and verify the concurrency settings. - Use the automation to alert you if the performance drops below a certain threshold. By following these steps, you can determine a safe and efficient concurrency level for your Dagster jobs. Remember that benchmarking is an iterative process, and you may need to adjust your con