JIRA: https://sagebionetworks.jira.com/browse/PLFM-510 (description outdated, left for reference).
Related JIRAs:
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Whenever a file is uploaded though synapse the reference to the file is maintained in an index, which is a table in an RDS MySQL instance. We refer to a record in this index table as a File Handle. A file handle is simply a pointer to where the physical data is stored and does not provide context where the data is actually used and for this reason there is no explicit access model and instead the user that created the file handle “owns” it (we only allow the owner to delete the file handle or access it directly). Additionally from a user perspective a file handle is immutable and its metadata cannot be updated, this simplifies the internal handling of files in Synapse and provides a generic abstraction over file management.
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Once one or more scans are performed we end up with a log with the last time a file handle was seen as linked. This data will live in S3 and can be queried with Athena. We can now use this information to check if any file handle has not being linked for more than a given amount of time, assuming that a scan was performed recently. Note that we can consider file handles that have been modified before a certain amount of time E.g. we consider only file handles modified more than 30 days ago, this provides a window of for scanning associations multiple times.
Since the file handles are in the main DB we need a way to get this information and we could simply run several queries with Athena with a batch of ids. This is not ideal as it’s a slow process with several limitations, Athena queries are limited in size (e.g. we can probably ask for a 10K batch of ids), we have a limit on the number of concurrent queries that can be performed (e.g. 20/s) and handling the results is tricky (e.g. queries are asynchronous). Where Athena shines is when a single query is performed on a big dataset.
We can instead periodically export the file handle table in S3 and join the data directly using Athena. We can write a script that weekly does the following:
Restore a recent snapshot of the DB
Run a Glue job on the snapshot to export the file handle table in S3 (from an initial experiment this can take around 10-20 minutes) in parquet format, we can filter on the modification date (e.g. only file handles that have been modified 30 days prior the last scan was started) and avoid including previews (which are technically linked file handles).
Join this table with the data delivered by the kinesis stream to detect un-linked file handles to produce a new table in S3 with only the file handles that are un-linked
Send a message to the backend to inform that the results are ready, a worker can read this results in batches to populate a table with the un-linked file handle ids.
Note that given the uncertainty in the process (e.g. unexpected failures, missed scanned data etc) when a file handle is detected as un-linked we do not actually perform any other action. We can simply have a counter (or several records) that tracks how many times a file handle was detected as un-linked. This means that we can boost our confidence over time, for example if a file handle was detected as un-linked in 3 subsequent scans we can flag the file handles as UNLINKED and for subsequent ARCHIVAL. We can have a worker that periodically counts how many times a file was detected as un-linked, update the file handle status and remove the records from this table (e.g. to avoid reprocessing).
An interesting alternative approach to test would be to append the results in S3 directly (Athena allows to have an INSERT INTO kind of operation), building a log of un-linked detections, we can then run a query on this dataset directly in S3: for example we can group by the id of the file handle in the un-linked log over a window of the previous 30 days and count how many times the file handle was detected as un-linked, the result would effectively be the file handles that were detected as un-linked at least 3 times in the span of a month (assuming we run the detection often enough).
Un-linked File Handle Archival
Deleting user , we can reuse the current infrastructure that replay the change messages on file handles and keep exporting the file handle data in a dedicated glue table using kinesis.
The process is as follow (Note: the data resides in a dedicated bucket that automatically deletes objects after 30 days):
The file scanner continuously exports associations (currently every 6 days all the associations are scanned, both in prod and staging), using kinesis and a glue table to convert to parquet
A worker exports the file handle data, including id and updatedOn, using kinesis and a glue table to convert to parquet
Once a month (1st monday of the month at night) we run an Athena query that joins the two glue tables together to discover all the file handles that do not have an associations (left join with null check), the id of the query is sent to the backend so that results can be processed
A worker process the athena query results to flag the file handles as unlinked (only handles that are in the available status)
Considered Alternatives:
When we run the scanner we update a timestamp of the file handle directly in the DB, a worker will collect the file handles that have not been updated in more than 30 days and flag them as unlinked: This was the original option. It was dismissed because of the risk of trashing migration.
Keep a consistent list of links (e.g. add/remove links according to the object that work on file handles): We dismissed it because of the complexity of maintaining a reference count index, additionally we would duplicate data in the DB and migration would suffer from it.
Use of S3 infrastructure and objects tags: The idea was to use object tags and a lifecycle configuration, basically trying to tag objects when they were scanned. We dismissed this because updating a tag does not update the update date of the object and lifecycles in s3 buckets are based on it, we would have had to come up with a complex mechanism to update the lifecycle configuration periodically. Additionally the tagging operation costs would add up over time especially if scans are done frequently on all the objects.
See additional notes at
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Un-linked File Handle Archival
Deleting user data is a tricky business, especially if we cannot be 100% sure that the data is not used. Instead we propose an approach that goes in stages where first the un-linked data is detected but leaving it accessible and only after a certain amount of time we start archiving it and eventually delete it, with the option to restore it before it is actually garbage collected. The archived data will eventually deleted.
We considered various options for archiving the unlinked data:
Archive bucket + lifecycle
The unlinked file handles can be stored in a dedicated bucket
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The reason to move the data in a dedicated bucket is because we can defined life cycle policies at the bucket level keeping the data well organized.
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Storage cost is $0.0125/GB (vs standard is $0.023/GB for the first 50TB, $0.022/GB for the next 450TB and $0.021/GB for over 500TB): E.g. 1TB is ~$12.5 vs $25
PUT/POST/COPY/LIST cost is $0.01/1000 requests (vs standard $0.005/1000 requests): e.g. a million objects is ~$10 vs $5
GET/SELECT cost is $0.001/1000 requests (vs standard $0.0004/1000 requests): e.g. a million objects is ~$1 vs $0.4
Life Cycle cost $0.01/1000 requests (e.g. automatically delete): e.g. a million objects is ~$10
Fetch costs is $0.01/GB (e.g. if we want to restore): 1TB is ~$10
Storing the data in an archiving tier (e.g. glacier) could be an option but it complicates restores (first the objects needs to be restored) and access to data becomes expensive and slow. A new offering from AWS is the S3 Intelligent-Tiering that automatically manages and monitor usage patterns of objects keys to change the access tiers and while the fetching of the data has no costs associated we would pay ($0.1 per million objects/month) for monitoring, but for this use case it makes sense to directly store the data in the infrequent access storage class with custom life cycles to move it to other classes or delete the objects since the intelligent tiering class does not have (yet) fine grained control on how the data is moved between classes.
We can introduce a new STATUS column in the file handles:
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STATUS
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Description
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File Handle Accessible
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AVAILABLE
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Default status
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Yes
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UNLINKED
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The file handle has been identified as un-linked, if a pre-signed URL is requested for such an object we trigger an alarm (e.g. this should never happen unless we mistakenly identified a linked object)
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Yes, trigger alarm. Should we instead consider this as not accessible?
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ARCHIVING
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The file is being archived
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No, throw not found, trigger alarm
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ARCHIVED
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The file has been archived (e.g. moved from the original bucket to the archive bucket)
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No, throw not found, trigger alarm
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DELETED
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The file has been deleted, we can setup notifications in the S3 bucket and change the status when an object is deleted matching the key of the archived object(s). An alternative to deletion would be to store the objects in the S3 Glacier or Deep Archive for low long term storage costs.
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No, throw not found, trigger alarm
Additionally we keep track of the status update timestamp with a new column STATUS_TIMESTAMP. We use this timestamp to decide when to move an UNLINKED file handle to the archive. E.g. we can keep the file handle un-linked for 30 days before we move it to the archive.
A worker will simply periodically scan the UNLINKED file handles whose status was updated more than 30 days in the past:
The worker will check if other file handles that are still AVAILABLE use the same key (e.g. it’s a logical copy), in this case we cannot move the underlying data yet and the archival is delayed. In this case we can update the timestamp (so that the file handle is re-processed in the next 30 days). Note: this is a tricky edge case, we could delete the file handle at this point but then we only allow the 30 days window to restore it in the case of a copy. Maybe we can use a special status instead? What happen if the last copy is archived? What if this copy is restored then?
For eligible file handles the worker will set the status to ARCHIVING and send a message for another worker to start the archiving that will move the data to the archive bucket and set the status to ARCHIVED once done. We can keep the key of the file handle the same but prefixed with the source bucket (e.g. archive.bucket/proddata.sagebase.org/originalKey). In this way it’s easy to restore the file handle to the original key. Note that the previews can be deleted here, if a file handle is restored we can trigger the preview generation.
While a file handle is in the UNLINKED, ARCHIVING or ARCHIVED state it can be restored, the process would move back the data to the original key (if the object is in archiving tier such as glacier we first need to request the restore from AWS and the process is a bit more involved). We can introduce dedicated statuses (e.g. RESTORING → AVAILABLE) while the restore request is in progress.
Alternative Implementation
The process proposed above has the advantage of being mostly done externally to Synapse, but also introduce several point of failures that might be difficult to tackle correctly. Additionally it adds infrastructure and technologies that the backend team has limited knowledge of and in general it’s a substantial engineering effort that might span several months of development.
It’s worth mentioning a simpler alternative that reuses familiar technologies, especially considering the fact that from the bucket analysis what we are most concerned about is data linked in file entities and tables. The process might go as follow:
Every time a link is added (e.g. a file entity is created, a revision is updated etc) a dedicated service is invoked to inform the system about the association. We simply add a record to a dedicated table that maps the file handle with the object type/id, this should be done in the same transaction.
When a link is removed we invoke the service again to remove the link
Periodically we scan this table to detect unlinked linked file handles (e.g. file handles that do not have records in this table), we record this information in another table with with a timestamp.
Periodically we scan the “un-linked” table for file handles that have been un-linked for more than 30 days and update the file handles, we remove the record from this table
When a link is added we make sure that the the record is removed from the previous table as well
This is a simpler solution, the drawback is that we specifically have to “add the link” when we work with file handles and it adds a bit of overhead (e.g. additional insert to the DB), but we already do it one way or another. Forgetting to cleanup the links does not lead to any data loss. Additionally we can incrementally add the cleanup for each type if needed, for example we can focus on entities and tables only now. The deletion can be asynchronous, taking care of the deletion timestamp (e.g. delete only if the deletion timestamp is after any existing link), this is because deleting an entity or a table might lead to several thousands or millions of deletions that needs to be done in a background job.
Note that this would not necessarily replace the existing associations used for the access checks.
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starting with the S3 Standard - Infrequent Access storage class. A lifecycle configuration in the archive bucket can be used to move objects after a certain amount of time of their upload to cheaper storage classes and eventually deleted. The process would be as follow:
Periodically collect a batch of eligible UNLINKED file handles for which no copy that is still AVAILABLE exist and send a message to a dedicated SQS queue to archive the key
A worker uses the transfer manager to copy the object (if bigger than 128KB to avoid paying the overhead in the infrequent access class) to the destination bucket, the file handle status is updated accordingly
Through S3 notifications when a file handle is deleted by the lifecycle we can remove the file handle
Restoring an archived file handle in this case would involve in this case to check the storage class and perform a restore request if in an archive tier, process the S3 notifications when the objects is restored and move the object back to the original location.
Intelligent Tiering + tagging
Instead of using the infrequent access storage class, we can use the new intelligent tiering class (INT). This special class automatically enables monitoring of the objects for access and moves them automatically between standard and infrequent access according to access patterns. If an object is not accessed for 30 days it is moved automatically to INT-infrequent access. If the object is accessed again it is automatically moved back to INT-standard.
Additionally a new feature allows to enable a specific lifecycle configuration for the INT storage class that moves objects that have not been accessed for more than X days (min 90) to archive tier and after Y days (min 180) to deep archive tier. The archive tiers require a restore operation to access the objects that would move them to the standard class. Additionally this particular lifecycle can be setup to use a rule that uses object tags, so we can enable this only for UNLINKED data.The idea would be the following:
Move everything in our bucket to INT (we can use a lifecycle configuration for this, using 30 days to avoid moving multipart upload parts of ongoing uploads that are temporary and should not be moved to INT), objects that are smaller than 128KB are not moved (not cost effective)
Any object uploaded to our bucket is automatically uploaded as INT (iff > 128KB), this can be part of the storage location metadata
Setup a lifecycle configuration for INT so that objects tagged with some special value (e.g. synapse-status=UNLINKED) are moved to archive tier after 90 days and to deep archive tier after 180 days
Setup a worker that as in option 1. collects a batch of eligible UNLINKED file handles and tag them appropriately and set the status to ARCHIVED
For deletions we can have a worker that process the archived data older than 1 year. Given the recent development of the INT class (e.g. support for archive tier) I expect AWs to implement this for us (e.g. add in the lifecycle the option to delete objects similar to the normal lifecycles), we can wait 1 year before implementing this and see if amazon will deliver.
Restoring an archived file handle in this case would involve checking the storage class and perform a restore request if in the archive tier and process the S3 notification when the object is restored (no move involved, there is not temporary object for archived data in this case as it is moved automatically to the standard class).
There are several advantages of option 2. vs option 1: if our objects are all in INT we should see additional cost savings not only for UNLINKED data but also for data that is linked but not accessed while keeping the same durability and performance.
The fact that INT works on the access date rather than creation date is a good fit for our use case where we do not know the access patterns to our data. Additionally the INT class even though it moves object to infrequent storage does not have additional access/retrieval fees for objects while the infrequent access and archive classes such as glacier class can get expensive if we have to access the objects. The biggest advantage is that S3 moves the files around for us according to access date and tagging, we only have to tag objects that we want to move to the archive tier. The restore operation is also simpler (e.g. does not involve copying objects).
The only drawback is that the minimum billable time is 30 days, e.g. if we upload an object in INT and delete it before 30 days we are billed for the full 30 days but I do not think this would be a common scenario we might want to even support. We might want to avoid using INT for the dev account where we create a lot of temporary data and the first time we configure the lifecycle to migrate to INT make sure we set after the rule for 30 days after creation to avoid moving multipart uploads parts that are deleted soon after the upload.
Cost Analysis
In order to determine what is the best option and if it is worth deleting data we collected statistics about the unlinked data and made an estimate of the amount of data that is actually access (hot data) so that we can compare the various options, the following data was collected for the proddata.sagebase.org bucket (To see how refer to https://sagebionetworks.jira.com/wiki/spaces/PLFM/pages/1629782065/S3+Bucket+Analysis#%5BhardBreak%5DUnlinked-and-Hot-Data ):
Total Size: 748,386,690,923,161 (680.7 TiB)
Number of objects: 44,509,789
Hot Data Count* : 9,802,820
Hot Data Size*: 314,611,245,904,518 (286.1 TiB)
Hot Data (>= 128KB) Count: 4,555,052
Hot Data (>= 128KB) Size: 314,472,063,061,465 (286 TiB)
Unlinked Data Count: 8,762,805
Unlinked Data Size: 100,456,586,079,288 (91.36 TiB)
Unlinked Data Count (>= 128 KB): 2,823,189
Unlinked Data Size (>= 128 KB): 100,408,552,794,768 (91.32 TiB)
Monthly Unlinked Count (>= 128KB): 28,808
Monthly Unlinked Size (>= 128KB): 1,024,577,069,334 (0.93 TiB)
* We considered the file handles downloaded from tables and/or entities from 2020 till now. Does not include other type of downloads, so we will round up to 300TiB of hot data for estimates.
We estimated the monthly cost using the S3 pricing calculator and rounded to 680TiB the total amount of data(T), 300TiB of hot data (H) and 91TiB of unlinked data (U):
Leave the data in STD: $15,185
Move current unlinked (>=128 KB) to infrequent Access*: $14,394
Using the unlinked data >= 128KB: (680T - 91U) in standard + 91U in IA
Consider an avg of 28,808/month unlinked files for moving data to IA (PUTs cost)
Eventually move unlinked data to Glacier deep archive*: $13,323
Using the unlinked data >= 128KB: (680T - 91U) in standard + 91U in Glacier Deep Archive
Consider an avg of 28,808/month unlinked files for moving data to IA (PUTs cost)
Consider the avg of 28,808/month for lifecycle transitions to Glacier and Glacier Deep Archive
Move everything to INT**: $11,775
Consider 44% in INT-Standard (300H/680T)
Consider 56% in INT-IA ((680T-300H)/680T)
Includes the 44M objects monitoring fee
Move to INT + unlinked eventually in INT-deep archive**: $10,734
Assumes that unlinked data is part of the cold data
Consider 44% in INT-Standard (300H/680T)
Consider 43% in INT-IA ((680TD - 300H - 91U)/680T)
Consider 13% in INT-Deep Archive (91U/680T)
Includes the 44M objects monitoring fee
Assumes that we have 28,808 tags per month (GET/PUT request + tag costs)
Move to INT + delete unlinked data***: $10,544
Assumes that unlinked data is part of the cold data
Consider 50% in INT-Standard (300H/(680T - 91))
Consider 50% in INT-IA
Includes the 44M - 8M objects monitoring fee, no tags, no lifecycle transitions
* Note that it does not include the initial cost of moving 2M objects to IA
** Note that it does not include the initial fee for moving all the 44M objects to INT of about $400 (through a lifecycle transition), once an object is in INT there are no fees for other transitions. Additionally in order to detect UNLINKED file handles we use a few additional services heavily, such as kinesis firehose to stream data to S3, Athena for querying, Step Functions to orchestrate and EventBridge for triggering periodically. The service that is most expensive is kinesis firehose as we are pushing a lot of data through, checking the billing for the last few months though this adds up to a few tens of dollars so we excluded it from the estimates, see the cost explorer for kinesis.
*** Note that there is the possibility that a lot of data becomes unlinked in a single month (e.g. a large amount of data is copied in another bucket). We would pay the overhead of this data for a few months that will move to cheaper tiers and eventually archived into deep archive. Given that this is not a common use case (e.g. It might happen once or twice every few years), the complexities around how we store file handles and the current limitations of S3 we do not plan to support a “speed up” of the archival. The user can still request the deletion of their data through the appropriate APIs.
It is clear that just moving everything to INT is cost effective for our use case where our access patterns are unknown since we have data that even if still linked is rarely accessed (e.g. older projects, older versions of entities, older tables, messages etc). On top of it, archiving unlinked data might be worth it even though the cost savings is not comparable to just using the INT storage class, we have around 1TiB of data each month in average that gets unlinked.
Of note is that part of the unlinked data is temporary file handles that synapse creates internally (e.g. table queries, tables csv for uploads etc), the fact that each month we have such a high number (30K) of file handles that are unlinked might be attributed to this. Unfortunately we do not tag this kind of data so we do not know exactly how much of those 1TiB are actually temporary file handles (We do know that at least 20TiB of the current unlinked data is most likely due to this: https://sagebionetworks.jira.com/wiki/spaces/PLFM/pages/1629782065/S3+Bucket+Analysis#Temporary-File-Handles).
To solve this we should upload this data either with an special object tag that expires the object through a lifecycle transition or in a dedicated bucket (e.g. another storage location) with a lifecycle that expires everything after 30 days. This temporary objects could be flagged in the database with a special status (e.g. TEMPORARY) so that they are not collected as UNLINKED but rather removed either by S3 notifications (see open questions below) or through a worker.
Another clear point that surfaced is that deleting the unlinked data is not worth the effort, if we move it to the cheapest archive tiering it is actually worth keeping the file handles around just in case we need to recover them.
Proposed Implementation
Archival
In the FILES table we store the status of the file handle which is when created set as AVAILABLE, files that have been detected as UNLINKED are flagged as such. Due to the fact that we allow to create copies of the same file handle that points to the same key we have the following possible scenarios for a file that is flagged as UNLINKED:
At least one copy exists for the same key that is still AVAILABLE
All of the copies for the same key are flagged as UNLINKED
Note that a file handle can be copied (not the physical data) by different users through a dedicated API, in other words we have multiple file handles pointing to the same bucket and key that could have been “created” by different users.
We want to have a window of time (e.g. 30 days) before we actually archive an object, this provides an additional safety net against potential bugs and misdetections. If a file handle that is not AVAILABLE is accessed the system will send an alert for further investigation, additionally it will not be possible to perform certain operations on a file handle that is not AVAILABLE (e.g. cannot copy it, eventually we will disable any kind of access to not AVAILABLE file handles).
An additional aspect that we need to consider is that the staging database shares the same file handle pointers as production, but at the same time it might be in a state of partial synced truth with prod therefore it is not possible to have an automated scheduled job that is part of the stack to perform the archival.
Instead we propose to add a new dedicated (admin only) asynchronous job that will be invoked by an external process periodically (e.g. a jenkins job):
The request specifies a batch size and an amount in days to look backwards. This is needed to limit the amount of data that we need to scan per job and avoiding having expensive table scans (Maybe using a separate table for the status might be better, but we would have to refactor every file handle query to use a left join).
The worker will fetch a batch (LIMIT) of unique keys that:
Have been updated more than 30 days ago (let us call this point in time T) and no more than T - <the input days> (if we call this job every 10 minutes we can limit this to a day to avoid big scans)
Are flagged as UNLINKED
Whose bucket is proddata.sagebase.org (for now we process only our bucket, we can later extend it to other buckets, maybe through a storage location configuration)
A message is put on a queue that includes a batch of keys (e.g. 100) and T. Another worker will process each key in the message in a separate transaction, for each key:
Updates all the file handles for that key older than T and that are UNLINKED so that their status is ARCHIVED
Fetches the count of file handles for that key that are AVAILABLE OR (UNLINKED and have been updated after T)
If the count is > 0 then the key is still available or another file handle is unlinked but still not past the 30 days window
If the count is 0 then we can archive the object in S3: we first get the tag set of the object and then merge it with a new tag such as “synapse-status=archive”. With a configuration for the INT storage class that automatically moves the objects tagged as such in the archive tier and deep archive tier if they were not accessed for more than 90 and 180 days respectively.
All the previews of the ARCHIVED file handles can be deleted, as long as they are not used by other available file handles (yeah, this is unfortunately a possibility). The S3 object of the previews is deleted iff the file handle is the last pointing to its key
Note that since we do not care much about keeping track of the job itself the job completes when the batch has finished sending messages to the queue (e.g. not when all the keys in the batch have been processed). If we call this job again too frequently we might risk to put the same key in the queue multiple times but I don’t see any side effects.
Note that we do not delete file handles, in this way we allow them to be restored at a later time if needed by their ids (due to the fact that different users might have created copies of the same file handle). Additionally we want to skip tagging (and therefore moving them to the archive tiers) files smaller than 128KB for two reasons:
Even if we enable a lifecycle to automatically move objects to INT those will not be moved (we can upload new ones in the INT class though without limitation but they are not automatically move into INT-IA after 30 days)
The amount of data is irrelevant (a few GB) for being moved to the archive tier tagging it. We would pay more for the get/put requests and object tags than for standard storage (e.g. we have ~6M objects < 128KB, only the tags costs $6/month. Storing the 100GB in Standard is $2.5).
Restore
Since we do not actually delete the data nor the file handles but we potentially store them in an archive tier we need a way to perform a restore (once in the archive tier an object needs an async operation to restore objects that might take a few hours).
We propose to add a new dedicated API that performs the restore through an asynchronous job:
Takes in input a batch of ids (max 1000) to restore
For each id checks the file handle status (checking that the user is the creator or admin):
If UNLINKED updates it and all the matching file handles to AVAILABLE.
If ARCHIVED, fetch the archive status of the S3 object (a head request should be enough, or we can batch this into a list objects):
If none then the object is still in INT-IA or INT-Standard, the object can be updated as AVAILABLE along with all the copies and the synapse-status tag can be cleared from the object
If present updates it and the matching file handles to RESTORING and sends a restore request to the s3 object, this is an asynchronous call. We enable the S3 notifications and when a restore completes a worker will set the all the file handles that match the keys as AVAILABLE and the synapse-status tag can be cleared from the object
Any other status is preserved
Note that even though the job completes some of the file handles might still be RESTORING, for each file handle in input we will include the result in the output of the job with a restore_status: FORBIDDEN, NOT_FOUND, NO_ACTION, RESTORED, RESTORING. We do not wait for the actual restore of the archive tier files, because we would either need to refactor the asynchronous job machinery to support migratable jobs or add dedicated custom code. Hopefully we will never have to use this job.
Note that we need to make sure that when we flag a file handle as UNLINKED we only do so if the status is AVAILABLE and its current updatedOn is < 30 days otherwise we risk to put back the restored objects as UNLINKED.
Open questions
Why can we make “copies” of file handles in the first place? Maybe we should not allow it in S3 or GC buckets? Who is using this? If we get rid of this does it make it simpler? → ✅ This seems to be used heavily by scientists (from DW data), unrealistic to get rid of it and/or deduplicate them.
I have no idea when the last access date is updated for the INT class nor a way to test it quickly, does tagging the object reset this and moves the object back to INT-Standard? If so we might have a weird lifecycle where an object that is uploaded might go: INT-Standard → (Day 30) INT-IA → UNLINKED → (Day 60, tagging) INT-Standard → (Day 90) INT-IA → (Day 150) INT-Archive → (Day 240) INT-DeepArchive. Basically we might have a month where we go back paying the INT-Standard, in the long run the cost would be amortized since objects will eventually move to the archive tier. → ✅ I verified that tagging does NOT in fact push the object back to the INT-Standard. In my own bucket I have objects that are INT-IA, I tagged those objects from the console (therefore fetching the tags as well) and the metrics reported after a day did not change the count of object in INT-IA. This is great news as the lifecycle is now clear and more cost effective: INT-Standard → (Day 30) INT-IA → UNLINKED → (Day 60, Tagging) → (Day 90) INT-Archive → (Day 180) INT-DeepArchive.
Should we delete the copies of the file handles if at least one is AVAILABLE? If so, if all copies are UNLINKED should we just keep one around? Which one? → ✅ No, copies effectively make the ownership a 1 to N relationship
It is really not clear if lifecycle transitions generate S3 notifications, for example if we wanted to get a notification when a temporary object is automatically expired so that we can remove the file handle record. It looks like this was not possible a few years ago, but the reference in the documentation disappeared, we will have to test this or contact support.
I didn’t even start thinking about GC or other type of file handles. The vast majority of unlinked data is in prod though.
Unindexed Data
From the S3 Bucket Analysis it turns out that we have around 7.5TB (this figure didn’t change over time) of data in S3 for which there is no file handle.
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Unfinished multipart uploads: We currently store parts in temporary objects in S3 and the backend later copies the part over to the multipart upload and deletes the temporary parts when the multipart is completed, if the multipart upload is not finished the parts are never deleted. Additionally S3 keeps the multipart upload data “hidden” until the multipart is finished or aborted. As of November 10th we do have 1417823 uncompleted multipart uploads, of which 1414593 were initiated before October 11th (See S3 Bucket Analysis ). An rough estimate on the current unfinished multipart parts that were uploaded accounts for 2.4TB of data.
Data from staging: Data created in staging is removed after migrations, but of course the data in S3 is left intact.
Old data already present in the bucket and never cleaned up.
In this case the amount of data compared to the category of un-linked data is most likely irrelevant and it’s probably not worth tackling at the moment but still worth mentioning potential solutions. The first point with multi-part uploads is what might be more relevant but we can enable solutions to avoid future costs such as:
Enable a life cycle in the S3 bucket that automatically removes un-finished multipart uploads (See 2.4TB of data (This number went down to 600GB with the implementation of https://sagebionetworks.jira.com/browse/PLFM-6516 , we still have 1.5M multipart uploads not finished due to a bug in the code that was later fixed: https://sagebionetworks.jira.com/browse/PLFM-6462). This would also mean to expire our multipart uploads in the backend (e.g. if the life cycle deleted incomplete uploads after 2 months, the multipart uploads in the backend should be forcibly restarted for example after a month).
Refactor the multipart upload to avoid uploading to temporary objects in the bucket (See https://sagebionetworks.jira.com/browse/PLFM-6412)
We enabled the S3 inventory for our bucket and we can write a job that compares the file handle index with the inventory to delete un-indexed data. A potential approach is to write a job that:
Streams to S3 the keys of the file handles that point to the prod bucket that were created between two given dates (e.g. last time the job was run and a month in the past from now)
Use Athena to join such data on the latest inventory (filtering again by the given dates) to identify un-indexed data
Delete from S3 the un-indexed data (or move it to a low cost storage class bucket, with an automatic deletion policy)
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6523 ).
Data from staging: Data created in staging is removed after migrations, but of course the data in S3 is left intact.
Old data already present in the bucket and never cleaned up.
In this case the amount of data compared to the category of un-linked data is not as urgent as it does not seem to grow over time and it’s probably not worth tackling at the moment but still worth mentioning potential solutions. The first point with multi-part uploads is what might be more relevant but we can enable solutions to avoid future costs such as:
Automatically cleanup unfinished uploads (See https://sagebionetworks.jira.com/browse/PLFM-6516 )
Enable a life cycle in the S3 bucket that automatically removes old un-finished multipart uploads (See https://sagebionetworks.jira.com/browse/PLFM-6462).
Refactor the multipart upload to avoid uploading to temporary objects in the bucket (See https://sagebionetworks.jira.com/browse/PLFM-6412)
We enabled the S3 inventory for our bucket and we can write a job that compares the file handle index with the inventory to delete un-indexed data. A simple approach would be to run a script periodically that does the following:
Queries the inventory with Athena for a certain date range fetching keys in batches (making sure to always have an upper bound of T - 60 days to avoid collecting keys for temporary data such as the multipart upload parts). We can keep track of the date of the object in the last processed batch to be used as the next lower bound.
For each batch of keys asks the backend (e.g. through a dedicated admin API) which keys do not have file handles
Saves the response to disk
For each non matching key deletes the batch of objects (or add a tag so that a lifecycle could expire the objects)
In general we should make sure that any data that ends up in the prod bucket is actually indexed in file handles, for example I would move the temporary objects used for the multipart upload in its own dedicated bucket.
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