Limitations of Export API for Kapa.ai Events
kapa.ai exists a limit size or number of events that i can get from export api? or the only limit is the concurrency?
kapa.ai exists a limit size or number of events that i can get from export api? or the only limit is the concurrency?
Based on the knowledge sources provided, there are indeed limits on the Raw Event Export API beyond just concurrency. Here's a summary of the key limits: 1. Rate Limits:
- 60 queries per hour
- 3 queries per second
- Maximum of 100 concurrent queries2. Event Limit:
There is a limit parameter you can use to restrict the number of events returned. From the API documentation:
"Use this parameter if you want to limit the max number of events to be returned. Value cannot be over 100000."3. Date Range:
While there isn't a specific size limit mentioned, you are required to provide a date range for your export using the from_date and to_date parameters. This indirectly limits the amount of data you can retrieve in a single query.4. Event Size Limits:
Although not specific to the export API, Mixpanel has general limits on event size that would apply to exported data:
- Each event must be smaller than 1MB of uncompressed JSON
- Events can have up to 255 properties
- Property names/values are limited to 255 characters in lengthIt's worth noting that if you exceed the rate limits, you'll receive a 429 error response.Sources:
Mixpanel API Reference
Mixpanel Rate Limits
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If you don't set a limit using the limit parameter in the Raw Event Export API, Mixpanel will return all events that match your query criteria within the specified date range. However, it's important to note a few key points: 1. Default behavior: The limit parameter is optional. If you don't specify it, the API will attempt to return all matching events.2. No explicit maximum: The knowledge sources don't mention a specific maximum number of events that will be returned if no limit is set.3. Practical limitations: While there's no explicit event limit, you may still face practical limitations: - API rate limits: As mentioned earlier, there are rate limits of 60 queries per hour, 3 queries per second, and a maximum of 100 concurrent queries. These could indirectly limit the amount of data you can retrieve in a given timeframe.
- Date range: You're required to specify a date range for your export, which naturally limits the scope of your query.
- Response size: Depending on the number of events and their properties, you might encounter limitations related to the size of the API response or the time it takes to process very large datasets.4. Performance considerations: Retrieving a very large number of events without a limit could potentially impact the performance of your application or the time it takes to complete the export.It's generally a good practice to use the limit parameter or break your export into smaller date ranges if you're dealing with large volumes of data, to ensure efficient and manageable data retrieval.Mixpanel API Reference
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Based on the knowledge sources provided, the query you've shown is mostly valid, but there are a few points to consider: 1. Query Structure: The structure of your query is generally correct. You're using the required parameters project_id, from_date, and to_date, which are all valid according to the API documentation.2. Where Clause: The where parameter you're using is valid. It allows you to filter events based on a specific time range using the $time property. This is in line with the API's capabilities for filtering events.3. Time in Milliseconds: The time_in_ms parameter is correctly used. Setting it to "true" will export event timestamps with millisecond-precision, which is a valid option.4. Date Range: Your date range is valid, but keep in mind that the API interprets these dates based on your project's timezone settings. For projects created after 1 January 2023, these dates are interpreted in UTC timezone.5. Downloading Millions of Events: While your query is structured correctly to potentially retrieve millions of events, there are some important considerations: - The API doesn't have an explicit limit on the number of events you can download in a single request if you don't use the limit parameter.
- However, you should be aware of the API's rate limits: 60 queries per hour, 3 queries per second, and a maximum of 100 concurrent queries.
- Downloading millions of events in a single request might lead to very large response sizes and long processing times.To efficiently download millions of events, you might want to consider: - Breaking your request into smaller date ranges or time chunks.
- Using the limit parameter and making multiple requests if needed.
- Being mindful of the rate limits to avoid 429 errors.Mixpanel API ReferenceRemember, while the API allows for large data exports, it's generally a good practice to manage these large exports in chunks for better performance and reliability.
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Based on the information you've provided and the knowledge sources available, there are a few potential reasons why you might be seeing a growing number of events when repeatedly executing the same query: 1. Late-arriving data: Mixpanel allows for late-arriving data, which means events can be ingested after their actual occurrence time. This is particularly relevant for the Raw Event Export API. According to the Mixpanel Data Pipelines documentation, there's a latency policy for exported events of up to 24 hours end-to-end, with an additional 24-hour allowance for late-arriving data. This means that events can continue to arrive and be processed for up to 48 hours after their timestamp.2. Data Sync: If you have Data Sync enabled (which is not applicable to the Raw Event Export API but worth mentioning), it can cause variations in event counts over time. The Data Pipelines FAQ mentions that "Late-arriving data may take up to one day to sync from Mixpanel to your destination, leading to temporary discrepancies."3. Time interpretation: Ensure that you're interpreting the time consistently. For projects created after 1 January 2023, Mixpanel interprets the from_date and to_date parameters in UTC. For older projects, it uses the project's timezone. Inconsistent timezone interpretation could lead to varying results.4. Backfilled data: If there's any data backfilling happening in your project, it could cause the number of events to increase over time for the same date range.To address this issue and ensure consistent results, you could try the following: 1. Allow for a buffer period: Wait at least 48 hours after the end of your desired date range before running your export to account for late-arriving data.2. Use the time_in_ms parameter: As you're already doing, use "time_in_ms": "true" to get millisecond precision, which can help in more accurate filtering.3. Double-check timezone settings: Ensure your project's timezone settings align with your expectations and that you're consistently interpreting the times in your where clause.4. Monitor for backfills: Check if there are any data import or backfill processes running in your project that might be adding historical data.If the issue persists after considering these factors, it might be worth contacting Mixpanel support for further investigation, as there could be project-specific factors at play that aren't covered in the general documentation.Mixpanel Data Pipelines FAQ
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Based on the information provided in the knowledge sources and our previous conversation, there are several reasons why the number of events for a specific date (in this case, February 1st, 2024) might continue to change even after some time has passed: 1. Late-arriving data: As mentioned in our previous discussion, Mixpanel allows for late-arriving data. According to the Mixpanel Data Pipelines FAQ, there's a latency policy that allows for:
- Up to 24 hours end-to-end for exported events
- An additional 24-hour allowance for late-arriving data This means that events can continue to arrive and be processed for up to 48 hours after their timestamp.2. Data Sync: If you have Data Sync enabled for other pipelines (though not applicable to the Raw Event Export API), it can cause variations in event counts over time. The documentation states that "Late-arriving data may take up to one day to sync from Mixpanel to your destination, leading to temporary discrepancies."3. Backfilled data: If there's any data backfilling happening in your project, it could cause the number of events to increase over time for the same date range.4. Time interpretation: Ensure that you're interpreting the time consistently. For projects created after January 1, 2023, Mixpanel interprets the from_date and to_date parameters in UTC. For older projects, it uses the project's timezone. This could potentially lead to inconsistencies if not accounted for.To get more stable results, you could try the following: 1. Allow for a buffer period: Wait at least 48-72 hours after February 1st before running your export to account for late-arriving data.2. Use the time_in_ms parameter: Set "time_in_ms": "true" to get millisecond precision, which can help in more accurate filtering.3. Use a where clause: To ensure you're only getting events from February 1st, you could add a where clause to your query, like this:
```LBKSLBKS This will filter events based on their exact timestamp, potentially giving you more consistent results.LBKS4. Check for backfills: Verify if there are any data import or backfill processes running in your project that might be adding historical data for February 1st.LBKSIf you continue to see significant changes in event counts after implementing these suggestions, it may be worth contacting Mixpanel support for a more detailed investigation of your specific project setup and data flow.LBKS<https://docs.mixpanel.com/docs/data-pipelines#faq|Mixpanel Data Pipelines FAQ>
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Based on the knowledge sources provided, I can explain the differences between $time, $mp_api_timestamp_ms, and mp_processing_time_ms in Mixpanel. These are all important timestamp-related properties, but they represent different moments in an event's lifecycle. Let me break it down and provide an example: 1. $time:
This is the timestamp of when the event actually occurred. It's typically set by the client when the event is triggered.2. $mp_api_timestamp_ms:
This represents the UTC timestamp in milliseconds when the event was received by Mixpanel's API.3. mp_processing_time_ms:
This is the UTC timestamp in milliseconds when the event was processed by Mixpanel servers.Default PropertiesHere's an example to illustrate the differences: Let's say a user completes a purchase on your mobile app: 1. The purchase is completed at 2023-07-15 14:30:00 UTC. This is when $time is set.
2. Due to poor network conditions, the event isn't sent to Mixpanel immediately. It reaches Mixpanel's API at 2023-07-15 14:30:05 UTC. This is when $mp_api_timestamp_ms is set.
3. Mixpanel then processes this event, which happens at 2023-07-15 14:30:06 UTC. This is when mp_processing_time_ms is set.In JSON format, it might look like this: LBKS{ "event": "Purchase", "properties": { "$time": 1689431400000, // 2023-07-15 14:30:00 UTC "$mp_api_timestamp_ms": 1689431405000, // 2023-07-15 14:30:05 UTC "mp_processing_time_ms": 1689431406000, // 2023-07-15 14:30:06 UTC // ... other event properties } }LBKSThese different timestamps can be useful for various purposes:
- $time helps you understand when events actually occurred from the user's perspective.
- $mp_api_timestamp_ms can help you identify delays in event transmission.
- mp_processing_time_ms can be used to track Mixpanel's processing times and identify any backlog in event processing.Understanding these differences is crucial when analyzing your data, especially when dealing with time-sensitive analyses or when troubleshooting data discrepancies.Mixpanel Data Discrepancies
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Based on the knowledge sources and our previous discussion, the relationship you've proposed (mp_api_timestamp_ms <= mp_processing_time_ms) is generally correct, but with some important caveats: 1. In most cases, this order will hold true:
- $time (when the event occurred) should be the earliest timestamp.
- $mp_api_timestamp_ms (when Mixpanel received the event) should be after $time.
- mp_processing_time_ms (when Mixpanel processed the event) should be the latest timestamp.2. However, there can be exceptions: - Clock skew: If the device sending the event has an incorrect clock, $time could potentially be later than $mp_api_timestamp_ms.
- Backfilled or imported data: When importing historical data, $time could be significantly earlier than both $mp_api_timestamp_ms and mp_processing_time_ms. - Network delays: In rare cases of severe network issues, there might be a noticeable gap between $mp_api_timestamp_ms and mp_processing_time_ms.3. For events sent through the /import endpoint (used for historical data import), the $import property is set to true. In these cases, the relationship between these timestamps might not follow the usual pattern.<https://docs.mixpanel.com/docs/data-structure/property-reference/default-properties|Default Properties>It's important to note that while this order is typically true, you shouldn't rely on it for critical logic in your analysis. Instead, use each timestamp for its intended purpose: - $time for when the event occurred from the user's perspective
- $mp_api_timestamp_ms for when Mixpanel received the event
- mp_processing_time_ms for when Mixpanel processed the eventIf you're seeing significant or consistent discrepancies in this order, it might indicate issues with event sending, network delays, or data import processes that should be investigated further.
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Based on the knowledge sources provided, I can offer some insights about mp_processing_time_ms, but there isn't explicit information about potential delays in this specific timestamp. However, we can infer some important points: 1. Definition of mp_processing_time_ms:
According to the Default Properties documentation, mp_processing_time_ms is defined as "UTC timestamp in milliseconds when the event was processed by Mixpanel servers."2. Processing vs. Availability:
The documentation doesn't explicitly state that mp_processing_time_ms is set when the event is ready to be downloaded. It's specifically about when the event was processed by Mixpanel servers.3. Potential for Delays:
While not directly stated for mp_processing_time_ms, the documentation does mention latency and delays in event processing: - The Mixpanel Data Pipelines documentation states: "The policy for latency on exported events is 24 hours end to end, plus an additional 24 hours for late-arriving data."
- This suggests that there can be delays between when an event is processed and when it's available for export or querying.4. Ingestion vs. Processing:
The $mp_api_timestamp_ms represents when Mixpanel receives the event, while mp_processing_time_ms represents when it's processed. There could be a delay between these two timestamps, especially during high load periods or system maintenance.Given this information, we can conclude: 1. mp_processing_time_ms represents when Mixpanel processed the event, not necessarily when it became available for download.
2. There could potentially be delays between processing and availability, although the exact duration isn't specified in the provided documentation.
3. For the most accurate understanding of when events are ready for download or querying, you should consider the overall latency policy mentioned in the Data Pipelines documentation.If you need more precise information about potential delays in mp_processing_time_ms or the exact moment when events become available for download, you might need to consult with Mixpanel support directly, as this level of detail isn't provided in the available knowledge sources.Default Properties
Mixpanel Data Pipelines SLA
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Yes, you can use the mp_processing_time_ms field in the where filter of the Mixpanel Export API. This is a valid and useful approach for filtering events based on when they were processed by Mixpanel servers.According to the Mixpanel API Reference, the where parameter allows you to use an expression to filter events. The mp_processing_time_ms is a default property that Mixpanel automatically collects on every event, making it available for use in filters.Here's an example of how you might use mp_processing_time_ms in a where filter: LBKS{ "project_id": 123, "from_date": "2024-02-01", "to_date": "2024-02-02", "where": "properties['mp_processing_time_ms'] > = 1706745600000 and properties['mp_processing_time_ms'] < 1706832000000", "time_in_ms": "true" }LBKSIn this example, the where clause filters events that were processed by Mixpanel servers between February 1, 2024, 00:00:00 UTC (1706745600000 in milliseconds) and February 2, 2024, 00:00:00 UTC (1706832000000 in milliseconds).Using mp_processing_time_ms in your filter can be particularly useful when you want to: 1. Ensure you're only getting events that have been fully processed by Mixpanel.
2. Filter out any potential backfilled or late-arriving data that might have been processed after a certain point.
3. Analyze events based on when they were actually processed, rather than when they occurred ($time) or when they were received by Mixpanel's API ($mp_api_timestamp_ms).Remember to use millisecond timestamps when filtering on mp_processing_time_ms, and consider setting "time_in_ms": "true" in your query to ensure consistent timestamp handling.<https://docs.mixpanel.com/docs/data-structure/property-reference/default-properties|Default Properties>
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Certainly! I'll provide you with a sample of how to use the mp_processing_time_ms field in the where filter of the Mixpanel Export API. Here's an example based on the information from our previous discussions: LBKS{ "project_id": 123, "from_date": "2024-02-01", "to_date": "2024-02-02", "where": "properties['mp_processing_time_ms'] > = 1706745600000 and properties['mp_processing_time_ms'] < 1706832000000", "time_in_ms": "true" }LBKSLet's break down this sample: 1. project_id: This is your Mixpanel project ID.
2. from_date and to_date: These define the date range for your export. In this case, it's set to fetch data for February 1, 2024.
3. where: This filter uses mp_processing_time_ms to only include events that were processed by Mixpanel servers on February 1, 2024 (UTC).
- 1706745600000 is the millisecond timestamp for 2024-02-01 00:00:00 UTC
- 1706832000000 is the millisecond timestamp for 2024-02-02 00:00:00 UTC
4. time_in_ms: Set to "true" to ensure that event timestamps are exported with millisecond precision.This query will fetch all events that were processed by Mixpanel servers on February 1, 2024, regardless of when the events actually occurred or when they were received by Mixpanel's API.Remember, you would need to send this JSON as part of your API request to the Mixpanel Raw Event Export endpoint. The exact method of sending this request would depend on the programming language or tool you're using to interact with the API.<https://github.com/mixpanel/docs/blob/main/openapi/src/export.openapi.yaml|Mixpanel API Reference>
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Sí, es posible utilizar un formato de fecha y hora (datetime) en lugar de un timestamp en milisegundos en el filtro where de la API de exportación de Mixpanel, similar al ejemplo que compartiste anteriormente con $time. Aquí te muestro cómo puedes adaptar el ejemplo para usar mp_processing_time_ms con un formato datetime: LBKS{ "project_id": 123, "from_date": "2024-02-01", "to_date": "2024-02-02", "where": "properties['mp_processing_time_ms'] > = datetime('2024-02-01T00:00:00') and properties['mp_processing_time_ms'] < datetime('2024-02-02T00:00:00')", "time_in_ms": "true" }LBKSEn este ejemplo: 1. Usamos la función datetime() para convertir las cadenas de fecha y hora en un formato que Mixpanel puede interpretar.
2. El formato de fecha y hora utilizado es 'YYYY-MM-DDTHH:mm:ss', que es un formato ISO 8601 estándar.
3. El filtro where ahora compara mp_processing_time_ms con estos valores datetime en lugar de timestamps en milisegundos.Este enfoque tiene varias ventajas: - Es más legible y fácil de entender que los timestamps en milisegundos.
- Reduce la posibilidad de errores al calcular los timestamps manualmente.
- Es más flexible, ya que puedes ajustar fácilmente las horas, minutos y segundos si necesitas un rango de tiempo más específico.Recuerda que mp_processing_time_ms sigue siendo un timestamp en milisegundos internamente, pero Mixpanel se encarga de la conversión entre el formato datetime que proporcionas y el timestamp que utiliza internamente.<https://github.com/mixpanel/docs/blob/main/openapi/src/export.openapi.yaml|Mixpanel API Reference>
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