在此页面上：了解Journey Optimizer系统生成的数据集的生存时间保留限制，以便您可以规划跟踪、反馈和历程数据保持可用状态的时长，并在数据过期之前保留关键数据。
从 2025 年 2 月起，已在 新沙盒和新组织 中推出用于 Journey Optimizer 系统生成的数据集的生存时间 (TTL) 护栏，如下所示：
- 配置文件存储中的数据为 90 天，
- 数据湖中的数据为 13 个月。
此更改将从 2026年10月1日 开始在 现有客户沙盒 中实施。
受影响的数据集 datasets
下表列出了所有Journey Optimizer系统生成的数据集，以及它们在数据湖和配置文件存储区中的生存时间。 时间序列数据集受TTL限制；记录类型数据集列出以供参考，并在两列中标记为n/a。 可用性列指示默认情况下是否包含数据集，或者是否需要特定加载项或许可证。
常见问题 faq
您可以在下文中找到有关数据集生存时间(TTL)的常见问题解答。
需要更多信息？ 使用本页底部的反馈选项提出问题，或通过 Adobe Journey Optimizer 社区进行联系。
n/a。当前不支持TTL扩展。 但是，已计划优化TTL流程，以便在2025年下半年开始的某个时候允许这些扩展请求。
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| NOTE |
| 存储在用户档案中的数据受总数据量权利文件的约束。 因此，因TTL扩展而导致配置文件上任何数据存储增加都将计入总数据卷权利中。 了解详情 |
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查找存储：否
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历程上限：否
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优惠上限：否
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发送时间优化(STO)：否
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消息频率上限（即业务规则）：否
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报告：否
note NOTE 已在Customer Journey Analytics (CJA)连接上实施TTL，这将受影响的数据集数据的有效最大回顾周期减少到13个月。 -
Experience Platform数据源：不适用 — 不支持通过数据源检索Experience event。
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计算属性：是 — 初始回填计算将限制为过去90天的数据；计算属性将根据后续更新的增量事件进行更新。 一旦后续更新到达回顾时段（最多6个月），TTL就基本上不再影响计算属性。 了解详情。
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分段和重新定位：是 — 分段依赖于个人资料存储中的数据；因此，对受影响的数据集数据的回溯限制为90天。
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跟踪：是 — 将受影响的数据集数据的有效最大回溯时段减少到90天。 来自受影响的数据集的数据在数据湖中保留13个月。
- 审查您的用例，确定在新的TTL之外需要保留数据的任何用例。
- 设置自动查询，以便在删除数据之前将关键数据复制到派生的数据集。
- 与您的Adobe代表合作，讨论任何额外需求或潜在的TTL扩展（计划在未来版本中）。
Journey Optimizer系统生成的数据集受到保护，无法通过标准Adobe Experience Platform UI将其删除。 这些数据集对于Journey Optimizer的功能至关重要，由系统管理。
如果您需要永久删除Journey Optimizer系统数据集（例如，用于QA环境、沙盒清理或特定数据卫生要求），请联系Adobe工程部门或Adobe客户关怀部门。 这些数据集需要专门的后端过程，以确保完全安全地删除。
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| 对于这些系统数据集内的例行数据清理，请使用Privacy Service提供的 数据生命周期 操作来删除特定记录或标识。 了解详情 |
This section contains structured knowledge intended to support interpretation, retrieval, and question answering related to this topic.
For complete understanding, this information should be combined with the documentation on this page. Neither source is intended to stand alone; the page describes the feature, while this section provides additional context that helps disambiguate terminology, intent, applicability, and constraints.
- TL;DR: This page explains the time-to-live (TTL) retention guardrails on Journey Optimizer system-generated datasets in the profile store (90 days) and the data lake (13 months), including rollout timing and options for retaining data longer.
Intents:
- Understand how long system-generated dataset data is retained in the profile store versus the data lake.
- Identify which datasets are subject to TTL.
- Plan for the enforcement on existing customer sandboxes starting October 1, 2026.
- Retain data beyond the TTL using dataset export through Destinations or Data Distiller derived datasets.
- Understand the impact on segmentation, computed attributes, tracking, and Customer Journey Analytics.
Glossary:
- Time-to-live (TTL): retention guardrail after which system-generated dataset data is dropped (product-specific)
- Profile store: store subject to the 90-day TTL for impacted datasets (product-specific)
- Data lake: store subject to the 13-month TTL for impacted datasets (product-specific)
- Time-series dataset: dataset type that is subject to TTL
- Record-type dataset: dataset type not subject to TTL (marked
n/ain both TTL columns) - Data Distiller: entitlement that allows creating derived datasets stored in the data lake without a TTL (product-specific)
Guardrails:
- Profile store TTL: 90 days for Journey Optimizer system-generated dataset data (hard limit; TTL extensions are not currently supported).
- Data lake TTL: 13 months for Journey Optimizer system-generated dataset data (hard limit; TTL extensions are not currently supported).
- AJO Message Export Dataset and AJO Message Event Metadata Dataset: 30 days data lake TTL (hard limit; both require the Message Export add-on).
- TTL is enforced on new sandboxes and new organizations as of February 2025, and on existing customer sandboxes starting October 1, 2026 (hard enforcement date).
- TTL applies only to time-series datasets; record-type datasets are
n/ain both the Data Lake TTL and Profile Store TTL columns. - TTL enforcement uses the event timestamp, not the ingestion date.
- Journey Optimizer system-generated datasets are protected and cannot be deleted through the standard Adobe Experience Platform UI.
- Computed attributes: initial backfill calculation is limited to the last 90 days of data; subsequent look-back is a maximum of 6 months.
- Segmentation and retargeting look-back is limited to 90 days on affected profile store data.
Terminology:
- Canonical name: Time-to-live — Acronym: TTL
- Do not confuse: “profile store TTL” (90 days) ≠ “data lake TTL” (13 months)
- Do not confuse: “time-series dataset” (subject to TTL) ≠ “record-type dataset” (
n/a, not subject to TTL) - Do not confuse: dataset TTL (drops system-generated dataset data in the profile after 90 days) ≠ deletion of the profiles themselves (the profiles are not dropped)
FAQ:
- Q: Which datasets are subject to TTL? — Only time-series datasets; record-type datasets (such as entity and classification datasets) are not subject to TTL and are marked
n/a. - Q: Does the 90-day profile store TTL delete profiles? — No; the system-generated dataset data in the profile is dropped after 90 days, not the profiles themselves.
- Q: Can I increase the TTL? — TTL extensions are not currently supported; you can export data through Destinations, or, with a Data Distiller entitlement, create derived datasets stored without a TTL.
- Q: When does enforcement reach existing sandboxes? — Starting October 1, 2026.
- Q: Which timestamp is used for enforcement? — The event timestamp, not the ingestion date.
- Q: Can I delete Journey Optimizer system-generated datasets? — No; they are protected and cannot be deleted through the standard Adobe Experience Platform UI; contact Adobe Engineering or Adobe Customer Care for permanent removal.