Why do we have warm buckets?

[lawrence_magpoc]

Can't hot bucket just roll directly to cold bucket? Or it's not possible? Does it have anything to do with the fact that the hot bucket is actively getting written to? Can anyone please shed some light on this on a technical level as I'm not getting the answer I'm looking for from the documentations. Thanks in advance.

[Aditi27]

In Splunk's indexing process, buckets move through different stages: hot, warm, cold, and eventually frozen. The movement from hot to cold is a managed and intentional process due to the roles these buckets play and their interaction with Splunk's underlying data architecture.

1. Hot Buckets: Actively Written

• Hot buckets are where Splunk is actively writing data. This makes them volatile because they are still receiving real-time events and may be indexed (compressed and organized) as part of ongoing ingestion.

• Technical Limitation: Because of their active state, they can't directly roll into cold storage, which is designed for more static, read-only data.

2. Warm Buckets: Transition to Stability

• Once a hot bucket reaches a certain size or the active indexing period ends, it is closed and then rolled into a warm bucket. This transition is important because warm buckets are no longer being written to, making them stable but still optimized for searching.

• Reason for the Warm Stage: The warm stage allows for efficient search and retrieval of data without impacting the performance of the write operations happening in hot buckets.

Why Hot Can't Skip Directly to Cold

• Active Writing: Hot buckets are being actively written to. If they were to move directly to cold, it would require Splunk to freeze and finalize the data too early, disrupting ongoing indexing operations.
• Search and Performance Impact: Splunk optimizes warm buckets for active searches and allows warm data to remain in a searchable, performant state. Cold buckets, being long-term storage, are not indexed for real-time or high-performance search, making it impractical to move hot data directly into cold without this intermediary warm phase.

Conclusion: The design of the bucket lifecycle (hot → warm → cold) in Splunk ensures that data remains both accessible and efficiently stored based on its usage pattern. The warm bucket stage is crucial because it marks the end of write operations while maintaining search performance before the data is pushed into more permanent, slower storage in cold buckets. Skipping this stage could cause inefficiencies and performance issues in both data ingestion and retrieval processes.

[PickleRick]

Sorry, but this is untrue. There is no change in bucket structure between warm and cold. It's just that the bucket is moved from one storage to another.

I suppose from the technical point of view the buckets could go from hot "directly" to cold but it would be a bit more complicated from the Splunk internals point of view. When the hot bucket is being rolled to warm it's indexing end and it gets renamed (which is an atomic operation) within the same storage unit.

Additionally, hot and warm buckets are rolled on a different basis. So technically, a bucket could roll from hot to warm because of hot bucket lifecycle parameters (especially maxDataSize) and immediately after (on next housekeeping thread pass) get rolled to cold because of reaching maximum warm buckets count.

[isoutamo]

I agree with @PickleRick about data optimization of bucket. Those warm and cold buckets are identically. Of course you could additionally configure tsindex reduction there, but it hasn't anything to do with warm -> cold movements.

Here is one old, but still mostly valid presentation about event lifecycle. https://conf.splunk.com/files/2017/slides/splunk-data-life-cycle-determining-when-and-where-to-roll-...
After one have read it, one probably understand this better.

[isoutamo]

Hi

even a hot and warm buckets are in same path those are fundamentally different. Hot buckets are open for writing, all other buckets are read only buckets. Another difference between those are that only hot buckets are “local” in SmartStore environment. All other buckets are stored remotely and only cache versions are locally.

Actually in S2 environment this is an excellent question are there any needs for separate warm and cold buckets or should there be only one type. But as we have still lot of none S2 environments where we really need to separate warm and cold buckets for cost and performance point of view, I think that there is no real reason to do things differently based on usage of S2.

There are described lifecycle of events on @jawahir007 ‘s response. You could read more from docs and some conf presentations about buckets and their life cycle.

r. Ismo

[lawrence_magpoc]

Thanks @isoutamo. This is very insightful.

[jawahir007]

Key Reasons for Using Different Buckets in Splunk:

1. Data Lifecycle Management:

   • Splunk categorizes buckets to handle data at different stages of its lifecycle. As data ages, it moves through different types of buckets:
     • Hot Buckets: Where the data is first written. These are actively being indexed.
     • Warm Buckets: Once hot buckets are full, they move to warm buckets. These are still searchable but no longer being written to.
     • Cold Buckets: As data ages, it moves to cold buckets. These contain older data and are stored on cheaper, slower storage, but are still searchable.
     • Frozen Buckets: Data that is moved out of Splunk, often archived or deleted based on retention policies. Frozen data is not searchable in Splunk unless thawed (restored).
   • This structure helps manage data efficiently and ensures that recent data is readily available while older data is archived or deleted based on retention policies.

2. Performance Optimization:

   • Splunk searches through recent (hot/warm) and historical (cold) data differently to optimize performance. By organizing data into different buckets, Splunk can prioritize newer data, which is searched more often, while minimizing resource usage on older data.
   • This improves search performance because Splunk doesn’t need to scan all data equally.

3. Efficient Resource Allocation:

   • Storing data in different types of buckets allows for resource optimization. For example:
     • Hot and Warm buckets typically reside on faster, more expensive storage (SSD or fast disks) to ensure quick access to recent data.
     • Cold buckets are stored on slower, cheaper storage, conserving resources while still keeping older data searchable.

4. Retention and Compliance:

   • Organizations often have different retention requirements for data. By using bucket configurations, Splunk allows you to retain data based on the bucket type. For instance, you might keep hot/warm data for a shorter period, and cold data for longer.
   • Frozen buckets can be used to archive data to long-term storage (or delete it) based on compliance requirements.

5. Data Recovery and Index Integrity:

   • If there’s an issue with the index or corruption, buckets help isolate and recover specific portions of the data without impacting the entire index.
   • Splunk can selectively roll back or restore data from buckets, which is easier than dealing with a single monolithic structure.

6. Search Granularity and Parallelism:

   • Different buckets allow Splunk to parallelize searches more effectively. When a search is performed, Splunk can search through hot, warm, and cold buckets in parallel, improving the speed of search execution.

7. Historical Data Archiving:

   • Frozen buckets enable you to offload older, less frequently accessed data to external storage or archive systems, allowing Splunk to manage historical data cost-effectively without overwhelming the system with too much data.

[lawrence_magpoc]

Thanks for the response. Sadly though, as comprehensive as it may be, it still doesn't quite answer my question as to why there has to be warm buckets when you could just roll the hot bucket straight to cold bucket. My guess is that it has to do with the hot bucket getting actively written to. I think because of that, Splunk can't move the hot bucket straight to another disk where the cold buckets reside, otherwise, the space taken up by the hot bucket can't be reclaimed, or something like that. I could be wrong but I just need someone to confirm it for me. Other than that, I think your answer can be quite helpful too.

[jawahir007]

Splunk uses warm and cold buckets primarily for financial benefits and effective data separation. Warm buckets store recent data on fast, expensive storage to ensure quick access for critical searches, optimizing performance for frequently accessed information. In contrast, cold buckets move older, less critical data to slower, cheaper storage, reducing overall storage costs. This separation ensures that high-cost storage is used only for data that requires rapid retrieval, while long-term data is retained cost-effectively. By balancing data storage based on its importance and access frequency, Splunk helps organizations control expenses while maintaining efficient data management.

[lawrence_magpoc]

I see. So it's really just about data separation.

I'm wondering though since you said this: "Warm buckets store recent data on fast, expensive storage to ensure quick access for critical searches, optimizing performance for frequently accessed information."

the same thing can be said for hot buckets too, right? I mean after all, hot and warm buckets share the same directory. I don't know. Maybe I just haven't quite fully grasped yet why there can't be warm buckets for other reasons beyond data separation.

[jawahir007]

In Splunk, hot buckets are where incoming data is actively written and indexed. These buckets hold the most recent data and are immediately searchable. Once a hot bucket reaches its size or time limit, it transitions into a warm bucket. Warm buckets store data that is no longer being written to but remains searchable.

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[ITWhisperer]

hot buckets are still being written to, warm buckets are not. Both are usually on fast (expensive) storage.

[lawrence_magpoc]

Maybe I should rephrase my question to this:

Why can't hot bucket roll straight to cold bucket?

I get that hot bucket is actively getting written to which is why I said in my post that that's what I'm thinking is why there has to be warm buckets in the first place, but all I've been told so far is that hot bucket is actively being updated and warm bucket is not which, I'm afraid, doesn't exactly answer the above question.

[ITWhisperer]

You appear to be missing part of the answer - hot and warm buckets are normally stored on expensive fast storage, whereas (in order to reduce costs) cold buckets are stored on cheaper slower storage. Using these distinctions, Splunk gives organisations the flexibility to manage the cost of their storage infrastructure.

[lawrence_magpoc]

@ITWhisperer that's true. But for me, it will only make sense if hot and warm buckets reside in separate disks

Please take a look at this

Hot: Used for high read/write operations. For this we need our best CPU/RAM nodes here, and we use SSD storage.
Warm: Lighter search, read only. We can have less powerful nodes here. Warm nodes can use very large spindle drives instead of SSD storage.

the above statement refers to another software, but just like Splunk, it also follows the hot-warm-cold architecture, so I figured it would be a good point of comparison. There, it was stated that hot and warm use different disks, which makes sense to me. On the other hand, Splunk hot and warm buckets share the same directory in the same disk, so I don't understand how is that exactly gonna save us cost (if cost management is part of the reason why there is warm bucket). That brings me back to my original question: what's the point of having warm bucket when we already have the hot bucket which is also searchable and, most importantly, resides in the same directory/disk.

Maybe I'm missing something here but that's what I'm hoping to find out by posting this question.

[ITWhisperer]

For Splunk, the cost saving is between hot/warm storage and cold storage. It sounds like, for this other software, if the hot and warm buckets are on different storage devices, moving the buckets between hot and warm is going to be processor and i/o intensive, whereas, moving files which are on the same *nix file system is fast and efficient as all that needs to be done is to point the warm file path to the same i-node on the file system as it occupied as a hot bucket and remove the hot bucket path (pointer) to the i-node. While the other software may appear to give you more flexibility, by putting the hot and warm bucket locations on different file systems (even if they were on the same physical device) would incur runtime costs and inefficiencies.