Neutron/OVN Database consistency

Neutron/OVN Database consistency

This document presents the problem and proposes a solution for the data consistency issue between the Neutron and OVN databases. Although the focus of this document is OVN this problem is common enough to be present in other ML2 drivers (e.g OpenDayLight, BigSwitch, etc…). Some of them already contain a mechanism in place for dealing with it.

Problem description

In a common Neutron deployment model there could have multiple Neutron API workers processing requests. For each request, the worker will update the Neutron database and then invoke the ML2 driver to translate the information to that specific SDN data model.

There are at least two situations that could lead to some inconsistency between the Neutron and the SDN databases, for example:

Problem 1: Neutron API workers race condition

In Neutron:
  with neutron_db_transaction:
       update_neutron_db()
       ml2_driver.update_port_precommit()
  ml2_driver.update_port_postcommit()

In the ML2 driver:
  def update_port_postcommit:
      port = neutron_db.get_port()
      update_port_in_ovn(port)

Imagine the case where a port is being updated twice and each request is being handled by a different API worker. The method responsible for updating the resource in the OVN (update_port_postcommit) is not atomic and invoked outside of the Neutron database transaction. This could lead to a problem where the order in which the updates are committed to the Neutron database are different than the order that they are committed to the OVN database, resulting in an inconsistency.

This problem has been reported at bug #1605089.

Problem 2: Backend failures

Another situation is when the changes are already committed in Neutron but an exception is raised upon trying to update the OVN database (e.g lost connectivity to the ovsdb-server). We currently don’t have a good way of handling this problem, obviously it would be possible to try to immediately rollback the changes in the Neutron database and raise an exception but, that rollback itself is an operation that could also fail.

Plus, rollbacks is not very straight forward when it comes to updates or deletes. In a case where a VM is being teared down and OVN fail to delete a port, re-creating that port in Neutron doesn’t necessary fix the problem. The decommission of a VM involves many other things, in fact, we could make things even worse by leaving some dirty data around. I believe this is a problem that would be better dealt with by other methods.

Proposed change

In order to fix the problems presented at the Problem description section this document proposes a solution based on the Neutron’s revision_number attribute. In summary, for every resource in Neutron there’s an attribute called revision_number which gets incremented on each update made on that resource. For example:

$ openstack port create --network nettest porttest
...
| revision_number | 2 |
...

$ openstack port set porttest --mac-address 11:22:33:44:55:66

$ mysql -e "use neutron; select standard_attr_id from ports where id=\"91c08021-ded3-4c5a-8d57-5b5c389f8e39\";"
+------------------+
| standard_attr_id |
+------------------+
|             1427 |
+------------------+

$ mysql -e "use neutron; SELECT revision_number FROM standardattributes WHERE id=1427;"
+-----------------+
| revision_number |
+-----------------+
|               3 |
+-----------------+

This document proposes a solution that will use the revision_number attribute for three things:

  1. Perform a compare-and-swap operation based on the resource version
  2. Guarantee the order of the updates (Problem 1)
  3. Detecting when resources in Neutron and OVN are out-of-sync

But, before any of points above can be done we need to change the networking-ovn code to:

#1 - Store the revision_number referent to a change in OVNDB

To be able to compare the version of the resource in Neutron against the version in OVN we first need to know which version the OVN resource is present at.

Fortunately, each table in the OVNDB contains a special column called external_ids which external systems (like Neutron/networking-ovn) can use to store information about its own resources that corresponds to the entries in OVNDB.

So, every time a resource is created or updated in OVNDB by networking-ovn, the Neutron revision_number referent to that change will be stored in the external_ids column of that resource. That will allow networking-ovn to look at both databases and detect whether the version in OVN is up-to-date with Neutron or not.

#2 - Ensure correctness when updating OVN

As stated in Problem 1, simultaneous updates to a single resource will race and, with the current code, the order in which these updates are applied is not guaranteed to be the correct order. That means that, if two or more updates arrives we can’t prevent an older version of that update to be applied after a newer one.

This document proposes creating a special OVSDB command that runs as part of the same transaction that is updating a resource in OVNDB to prevent changes with a lower revision_number to be applied in case the resource in OVN is at a higher revision_number already.

This new OVSDB command needs to basically do two things:

1. Add a verify operation to the external_ids column in OVNDB so that if another client modifies that column mid-operation the transaction will be restarted.

A better explanation of what “verify” does is described at the doc string of the Transaction class in the OVS code itself, I quote:

Because OVSDB handles multiple clients, it can happen that between the time that OVSDB client A reads a column and writes a new value, OVSDB client B has written that column. Client A’s write should not ordinarily overwrite client B’s, especially if the column in question is a “map” column that contains several more or less independent data items. If client A adds a “verify” operation before it writes the column, then the transaction fails in case client B modifies it first. Client A will then see the new value of the column and compose a new transaction based on the new contents written by client B.

2. Compare the revision_number from the update against what is presently stored in OVNDB. If the version in OVNDB is already higher than the version in the update, abort the transaction.

So basically this new command is responsible for guarding the OVN resource by not allowing old changes to be applied on top of new ones. Here’s a scenario where two concurrent updates comes in the wrong order and how the solution above will deal with it:

Neutron worker 1 (NW-1): Updates a port with address A (revision_number: 2)

Neutron worker 2 (NW-2): Updates a port with address B (revision_number: 3)

TXN 1: NW-2 transaction is committed first and the OVN resource now has RN 3

TXN 2: NW-1 transaction detects the change in the external_ids column and is restarted

TXN 2: NW-1 the new command now sees that the OVN resource is at RN 3, which is higher than the update version (RN 2) and aborts the transaction.

There’s a bit more for the above to work with the current networking-ovn code, basically we need to tidy up the code to do two more things.

  1. Consolidate changes to a resource in a single transaction.

This is important regardless of this spec, having all changes to a resource done in a single transaction minimizes the risk of having half-changes written to the database in case of an eventual problem. This should be done already but it’s important to have it here in case we find more examples like that as we code.

2. When doing partial updates, use the OVNDB as the source of comparison to create the deltas.

Being able to do a partial update in a resource is important for performance reasons; it’s a way to minimize the number of changes that will be performed in the database.

Right now, some of the update() methods in networking-ovn creates the deltas using the current and original parameters that are passed to it. The current parameter is, as the name says, the current version of the object present in the Neutron DB. The original parameter is the previous version (current - 1) of that object.

The problem of creating the deltas by comparing these two objects is because only the data in the Neutron DB is used for it. We need to stop using the original object for it and instead we should create the delta based on the current version of the Neutron DB against the data stored in the OVNDB to be able to detect the real differences between the two databases.

So in summary, to guarantee the correctness of the updates this document proposes to:

  1. Create a new OVSDB command is responsible for comparing revision numbers and aborting the transaction, when needed.
  2. Consolidate changes to a resource in a single transaction (should be done already)
  3. When doing partial updates, create the deltas based in the current version in the Neutron DB and the OVNDB.

#3 - Detect and fix out-of-sync resources

When things are working as expected the above changes should ensure that Neutron DB and OVNDB are in sync but, what happens when things go bad ? As per Problem 2, things like temporarily losing connectivity with the OVNDB could cause changes to fail to be committed and the databases getting out-of-sync. We need to be able to detect the resources that were affected by these failures and fix them.

We do already have the means to do it, similar to what the ovn_db_sync.py script does we could fetch all the data from both databases and compare each resource. But, depending on the size of the deployment this can be really slow and costy.

This document proposes an optimization for this problem to make it efficient enough so that we can run it periodically (as a periodic task) and not manually as a script anymore.

First, we need to create an additional table in the Neutron database that would serve as a cache for the revision numbers in OVNDB.

The new table schema could look this:

Column name Type Description
standard_attr_id Integer Primary key. The reference ID from the standardattributes table in Neutron for that resource. ONDELETE SET NULL.
resource_uuid String The UUID of the resource
resource_type String The type of the resource (e.g, Port, Router, …)
revision_number Integer The version of the object present in OVN
acquired_at DateTime The time that the entry was create. For troubleshooting purposes
updated_at DateTime The time that the entry was updated. For troubleshooting purposes

For the different actions: Create, update and delete; this table will be used as:

  1. Create:

In the create_*_precommit() method, we will create an entry in the new table within the same Neutron transaction. The revision_number column for the new entry will have a placeholder value until the resource is successfully created in OVNDB.

In case we fail to create the resource in OVN (but succeed in Neutron) we still have the entry logged in the new table and this problem can be detected by fetching all resources where the revision_number column value is equal to the placeholder value.

The pseudo-code will look something like this:

def create_port_precommit(ctx, port):
    create_initial_revision(port['id'], revision_number=-1,
                            session=ctx.session)

def create_port_postcommit(ctx, port):
    create_port_in_ovn(port)
    bump_revision(port['id'], revision_number=port['revision_number'])
  1. Update:

For update it’s simpler, we need to bump the revision number for that resource after the OVN transaction is committed in the update_*_postcommit() method. That way, if an update fails to be applied to OVN the inconsistencies can be detected by a JOIN between the new table and the standardattributes table where the revision_number columns does not match.

The pseudo-code will look something like this:

def update_port_postcommit(ctx, port):
    update_port_in_ovn(port)
    bump_revision(port['id'], revision_number=port['revision_number'])
  1. Delete:

The standard_attr_id column in the new table is a foreign key constraint with a ONDELETE=SET NULL set. That means that, upon Neutron deleting a resource the standard_attr_id column in the new table will be set to NULL.

If deleting a resource succeeds in Neutron but fails in OVN, the inconsistency can be detect by looking at all resources that has a standard_attr_id equals to NULL.

The pseudo-code will look something like this:

def delete_port_postcommit(ctx, port):
    delete_port_in_ovn(port)
    delete_revision(port['id'])

With the above optimization it’s possible to create a periodic task that can run quite frequently to detect and fix the inconsistencies caused by random backend failures.

Note

There’s no lock linking both database updates in the postcommit() methods. So, it’s true that the method bumping the revision_number column in the new table in Neutron DB could still race but, that should be fine because this table acts like a cache and the real revision_number has been written in OVNDB.

The mechanism that will detect and fix the out-of-sync resources should detect this inconsistency as well and, based on the revision_number in OVNDB, decide whether to sync the resource or only bump the revision_number in the cache table (in case the resource is already at the right version).

Refereces

Alternatives

Journaling

An alternative solution to this problem is journaling. The basic idea is to create another table in the Neutron database and log every operation (create, update and delete) instead of passing it directly to the SDN controller.

A separated thread (or multiple instances of it) is then responsible for reading this table and applying the operations to the SDN backend.

This approach has been used and validated by drivers such as networking-odl.

An attempt to implement this approach in networking-ovn can be found here.

Some things to keep in mind about this approach:

  • The code can get quite complex as this approach is not only about applying the changes to the SDN backend asynchronously. The dependencies between each resource as well as their operations also needs to be computed. For example, before attempting to create a router port the router that this port belongs to needs to be created. Or, before attempting to delete a network all the dependent resources on it (subnets, ports, etc…) needs to be processed first.
  • The number of journal threads running can cause problems. In my tests I had three controllers, each one with 24 CPU cores (Intel Xeon E5-2620 with hyperthreading enabled) and 64GB RAM. Running 1 journal thread per Neutron API worker has caused ovsdb-server to misbehave when under heavy pressure [1]. Running multiple journal threads seem to be causing other types of problems in other drivers as well.
  • When under heavy pressure [1], I noticed that the journal threads could come to a halt (or really slowed down) while the API workers were handling a lot of requests. This resulted in some operations taking more than a minute to be processed. This behaviour can be seem in this screenshot.
  • Given that the 1 journal thread per Neutron API worker approach is problematic, determining the right number of journal threads is also difficult. In my tests, I’ve noticed that 3 journal threads per controller worked better but that number was pure based on trial & error. In production this number should probably be calculated based in the environment, perhaps something like TripleO (or any upper layer) would be in a better position to make that decision.
  • At least temporarily, the data in the Neutron database is duplicated between the normal tables and the journal one.
  • Some operations like creating a new resource via Neutron’s API will return HTTP 201, which indicates that the resource has been created and is ready to be used, but as these resources are created asynchronously one could argue that the HTTP codes are now misleading. As a note, the resource will be created at the Neutron database by the time the HTTP request returns but it may not be present in the SDN backend yet.

Given all considerations, this approach is still valid and the fact that it’s already been used by other ML2 drivers makes it more open for collaboration and code sharing.

Footnotes

[1](1, 2) I ran the tests using Browbeat which is basically orchestrate Openstack Rally and monitor the machine’s usage of resources.
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