In a Cloudant database, what is the expected behavior of calling PUT on a document that doesn't exist with a revision defined?
The documentation says:
To update (or create) a document, make a PUT request with the updated
JSON content and the latest _rev value (not needed for creating new
documents) to https://$USERNAME.cloudant.com/$DATABASE/$DOCUMENT_ID.
I had assumed that if I did provide a revision, that the db would detect that it was not a match and reject the request. In my test cases I have inconsistent behavior. Most of the time I get the expected 409, Document update conflict. However, occasionally, the document ends up getting created (201), and assigned the next revision.
My test consists of creating a document and then using that revision to update a different document.
POST https://{url}/{db} {_id: "T1"} - store the returned revision
PUT https://{url}/{db}/T2 {_rev: }
So if the revision returned was something like 1-79c389ffdbcfe6c33ced242a13f2b6f2, then in the cases where the PUT succeeds, it returns the next revision (like 2-76054ab954c0ef41e9b82f732116154b).
EDIT
If I simplify the test to one step, I can also get different results.
PUT https://{url}/{db}/DoesNotExist {_rev: "1-ffffffffffffffffffffffffffffffff"}
Cloudant is an eventually consistent database. You're seeing the effects of that. Most of the time the cluster has time to reach a consistent state between your two api calls and you'll get the expected update conflict. Sometimes you hit the inconsistency window, as your first call has not yet been replicated around the cluster and you hit a different node. It's a valuable insight: it's not safe to read your writes.
Related
I have an application that goes like this:
ingestion --> queue --> validation --> persistance --> database
I want to load test the ingestion and at the end verify that every submitted entry is stored in the database.
I have an Artillery script that posts to ingestion and recovers the same item from the database, but it does so as part of the same scenario and since the two components are implemented separately I'm actually measuring a combined performance, instead of that of each component.
I would like to load test the ingestion component keeping hold of some search key that allow me to recover all sent items from the database. I've tried this by creating a Javascript that I call at the beginning of the ingestion scenario to generate a random search key, store it in Artillery's context and them at the end of the scenario call another function to recover all entries from the database.
The problem I found is that Artillery runs one copy of the scenario in each virtual client, so it calls the function each time it starts the scenario and recovers only one entry at the end. And the call to the database happens in the same scenario as the post to ingestion, so I'm again mixing performance.
What I would like to do, I suppose, would be to generate the search key in a scenario, run the posts in another scenario, and then retrieve the results in a third one. How can I do that?
Also, when I retrieve the results from the database, I would like to compare the quantity with the number of posts to ingestion. I couldn't find if expect works with variables returned in the context from function calls. Is this possible?
I don't believe this is possible. I have been reading the documentation and any examples I can find about Artillery scripts, and I don't see that there is any way to "chain" flows together.
I have a datastore entity called lineItems, which consists of individual line items to be invoiced. The users find the line items and attach a purchase order number to the line items. These are they displayed on the web page where they can create the invoice.
I would display my code for fetching the entities, but I don't think it matters at all as this also happened a couple times when I was using managed VM's a few months ago and the code is completely different. (I was using objectify before, now I am using the datastore API). In a nutshell, I am currently just using a StructuredQuery.setFilter(new PropertyFilter.eq("POnum",ponum)).setFilter(new PropertyFilter.eq("Invoiced", false)); (this is pseudo code you can't do two .setFilters like this. The real code accepts a list of PropertyFilters and creates a composite filter properly.)
What happened this morning was the admin person created the invoice, and all but two of the lines were on the invoice. There were two lines which the code never fetched, and those lines were stuck in the "invoices to create" section.
The admin person simply created the invoice again for the given purchase order number, but the second time it DID pick up the two remaining lines and created a second invoice.
Note that the entities were created/edited almost 24 hours before (when she assigned the purchase order number to them), so they were sitting in the database for quite a while. (I checked my logs). This is not a case where they were just created, and then tried to be accessed within a short period of time. It is also NOT a case of failing to update the entities - the code creates the invoice in a 3'rd party accounting package, and they simply were not there. Upon success of the invoice creation, all of the entities are then updated with "invoiced = true" and written in the datastore. So the lines which were not on the invoice in the accounting program are the ones that weren't updated in the datastore. (This is not a "smart" check either, it does not check line-by line. It simply checks if the invoice creation was successful or not, and then updates all of the entities that it has in memory).
As far as I can tell, the datastore simply did not return all of the entities which matched the query the first time but it did the second time.
There are approximately 40'000 lineItem entities.
What are the conditions which can cause a datastore fetch to randomly fail to grab all of the entities which meet the search parameters of a StructuredQuery? (Note that this also happened twice while using Objectify on the now deprecated Managed VM architecture.) How can I stop this from happening, or check to see if it has happened?
You may be seeing eventual consistency because you are not using an ancestor query.
See: https://cloud.google.com/datastore/docs/articles/balancing-strong-and-eventual-consistency-with-google-cloud-datastore/
My current understanding of Google AppEngine's High Replication DataStore is the following:
Gets and puts of individual entities are always strongly consistent, i.e. once a put of this entry completes, no later get will ever return a version earlier than the completed put. Or, more precisely, as soon as any one get returns the new version, no later get will ever return the old version again.
Gets and puts of multiple entities are strongly consistent, if they belong to the same ancestor group and are performed in a transaction, i.e. if I have two entities that are both being modified in a transaction by a put and "simultaneously" read in a different transaction with a get, the get will either return the old version of both entries or the new version of both entries, depending on whether the put-transaction has completed at the time of the get or not, but it will never return the old value of one entity and the new value of the other.
Queries with an ancestor filter can be chosen to be strongly or eventually consistent, where a strongly consistent query takes longer to complete, but will always return the "same" version (old or new) of all entities updated in the same transaction in this ancestor group and never some old and some new versions.
Queries that span ancestors are always eventually consistent, i.e. might return an old version of one result entity and a new version of another.
Did I get this right? Is this actually documented anywhere? (I only found some documentation about the query consistency here (between the first and second "Note") and here, but it doesn't talk about gets and puts...)
Yes, you're correct. They just word it slightly differently:
https://developers.google.com/appengine/docs/java/datastore/
Right at the beginning there are 5 point form features. The last two describe your question, except that they refer to "reads" instead of "gets".
This probably adds to your confusion, but when they mean "read" or "get", it really means fetching an entity directly - by key or id. If you call the python 'get' function with an attribute other than the key or id, it's actually issuing a query, which is eventually consistent (unless it's an ancestor query).
The way of resolving replication conflicts recommended by official documentation is:
Read conflicting revisions using document's _conflicts field (e.g. via a view)
Fetch docs for all revisions listed
Perform application-specific merging
Remove unwanted revisions
The problem comes in when I want to merge deleted documents. They do not show up in _conflicts field, but in _deleted_conflicts. If I merge only using _conflicts field, and a document is deleted in the local database and edited in the remote replica, it will be resurrected locally on replication. My application model assumes that deletion always takes precedence when merging: a deleted documents stays deleted regardless of what edits it conflicts with.
So, at a first glance, the simplest thing to do is to check that _deleted_conflicts is not empty and if it is not empty, delete the document, right? Well... the problem with this is that this may also contain deleted revisions that were introduced by resolving edit conflicts in step #4, so the meaning of _deleted_conflicts is ambiguous in this case.
What's the canonical way of handling deletion conflicts in CouchDB (if any) that doesn't involve doing gross things like marking documents as deleted and filtering at the application layer?
The best solution would be to use the reserved property _deleted to remove documents instead of HTTP DELETE. Then you are free to also set other properties:
doc._deleted = true;
doc.deletedByUser = true;
doc.save();
Then in the merge process check the _changes feed for _deleted_conflicts and delete the document if there is a revision within _deleted_conflicts that has the deletedByUser flag set to true.
I hope this helps!
I'm a little bit confused by the findAndModify method in MongoDB. What's the advantage of it over the update method? For me, it seems that it just returns the item first and then updates it. But why do I need to return the item first? I read the MongoDB: the definitive guide and it says that it is handy for manipulating queues and performing other operations that need get-and-set style atomicity. But I didn't understand how it achieves this. Can somebody explain this to me?
If you fetch an item and then update it, there may be an update by another thread between those two steps. If you update an item first and then fetch it, there may be another update in-between and you will get back a different item than what you updated.
Doing it "atomically" means you are guaranteed that you are getting back the exact same item you are updating - i.e. no other operation can happen in between.
findAndModify returns the document, update does not.
If I understood Dwight Merriman (one of the original authors of mongoDB) correctly, using update to modify a single document i.e.("multi":false} is also atomic. Currently, it should also be faster than doing the equivalent update using findAndModify.
From the MongoDB docs (emphasis added):
By default, both operations modify a single document. However, the update() method with its multi option can modify more than one document.
If multiple documents match the update criteria, for findAndModify(), you can specify a sort to provide some measure of control on which document to update.
With the default behavior of the update() method, you cannot specify which single document to update when multiple documents match.
By default, findAndModify() method returns the pre-modified version of the document. To obtain the updated document, use the new option.
The update() method returns a WriteResult object that contains the status of the operation. To return the updated document, use the find() method. However, other updates may have modified the document between your update and the document retrieval. Also, if the update modified only a single document but multiple documents matched, you will need to use additional logic to identify the updated document.
Before MongoDB 3.2 you cannot specify a write concern to findAndModify() to override the default write concern whereas you can specify a write concern to the update() method since MongoDB 2.6.
When modifying a single document, both findAndModify() and the update() method atomically update the document.
One useful class of use cases is counters and similar cases. For example, take a look at this code (one of the MongoDB tests):
find_and_modify4.js.
Thus, with findAndModify you increment the counter and get its incremented
value in one step. Compare: if you (A) perform this operation in two steps and
somebody else (B) does the same operation between your steps then A and B may
get the same last counter value instead of two different (just one example of possible issues).
This is an old question but an important one and the other answers just led me to more questions until I realized: The two methods are quite similar and in many cases you could use either.
Both findAndModify and update perform atomic changes within a single request, such as incrementing a counter; in fact the <query> and <update> parameters are largely identical
With both, the atomic change takes place directly on a document matching the query when the server finds it, ie an internal write lock on that document for the fraction of a millisecond that the server confirms the query is valid and applies the update
There is no system-level write lock or semaphore which a user can acquire. Full stop. MongoDB deliberately doesn't make it easy to check out a document then change it then write it back while somehow preventing others from changing that document in the meantime. (While a developer might think they want that, it's often an anti-pattern in terms of scalability and concurrency ... as a simple example imagine a client acquires the write lock then is killed while holding it. If you really want a write lock, you can make one in the documents and use atomic changes to compare-and-set it, and then determine your own recovery process to deal with abandoned locks, etc. But go with caution if you go that way.)
From what I can tell there are two main ways the methods differ:
If you want a copy of the document when your update was made: only findAndModify allows this, returning either the original (default) or new record after the update, as mentioned; with update you only get a WriteResult, not the document, and of course reading the document immediately before or after doesn't guard you against another process also changing the record in between your read and update
If there are potentially multiple matching documents: findAndModify only changes one, and allows you customize the sort to indicate which one should be changed; update can change all with multi although it defaults to just one, but does not let you say which one
Thus it makes sense what HungryCoder says, that update is more efficient where you can live with its restrictions (eg you don't need to read the document; or of course if you are changing multiple records). But for many atomic updates you do want the document, and findAndModify is necessary there.
We used findAndModify() for Counter operations (inc or dec) and other single fields mutate cases. Migrating our application from Couchbase to MongoDB, I found this API to replace the code which does GetAndlock(), modify the content locally, replace() to save and Get() again to fetch the updated document back. With mongoDB, I just used this single API which returns the updated document.