In Venice, there are many things which happen under the hood as part of Full Pushes. Besides serving to refresh data, Full Pushes also serve to make certain configs take effect, and also more generally keep the system tidy and healthy.
Many Venice users choose to periodically do Full Pushes for the sake of refreshing their data, in which case the auxiliary goals of Full Pushes end up being fulfilled organically.
In other cases, users do Full Pushes either infrequently, or not at all. In these cases, the operator may want to manually trigger a Repush, without involving the user.
The various motivations for operator-driven Repushes are described in more details below.
In Venice, some store configs can be changed live, while some others take effect when the next store-version is pushed. Configs which can be changed with a Repush (or a regular Full Push) include:
- Partition-related settings
- Partition count
- Amplification factor
- Custom partitioner implementation
- Replication settings
- Whether a store is hybrid
Having some configs be immutable within the scope of a store-version makes the development and maintenance of the system easier to reason about. In the past, the Venice team has leveraged Repushes to execute large scale migrations as new modes were introduced (e.g., migrating from active-passive to active-active replication, or migrating from the offline/bootstrap/online state model to the leader/follower state model). Repush enabled operators to roll out (and occasionally roll back) these migrations in a way that was invisible to users of the platform. Although these migrations are behind us, it is possible that future migrations may also be designed to be executed this way.
When doing a Full Push (and also when Repushing), the data gets sorted inside the Map Reduce job, prior to pushing. The Start of Push control message is annotated with the fact that the incoming data is sorted, which triggers an alternative code path within servers (and Da Vinci clients) in order to generate the storage files more efficiently.
When doing a Stream Reprocessing (SR), on the other hand, data does not get sorted. More generally, if the last Full Push happened a while ago, and lots of nearline writes happened since then, there could be a large portion of the data which is unsorted, and the operator may find that rebalance performance degrades as a result.
While it is not considered necessary to perform a Repush after every SR just for the sake of sorting, it can sometimes be useful if the last SR or Full Push happened quite a while ago.
In some cases where a datacenter (DC) suffered an outage, it is possible that pushes succeeded in some DCs and failed in others. Alternatively, it’s possible that correlated hardware failures resulted in some DC losing all replicas of some partitions. In these cases, as long as one of the DCs is healthy, it can be used as the source of a repush to repair the data in other DCs.
It’s usually required to evict stale entries from the store in order to achieve GDPR compliance or other business requirements. The repush with TTL functionality will replay and scan through the entries in the current version, and use store-level rewind time as TTL time to evict stale records based on the write timestamp. See TTL for the comparison among various TTL options.
The following Venice Push Job config is used to enable Repush functionality:
source.kafka = true
The following Venice Push Job configs provide more control over the Repush functionality:
To specify which Kafka topic (i.e. which store-version) to use as the source of the Repush. If unspecified, it will default to the highest available store-version.
kafka.input.topic = store-name_v7
To specify which datacenter to pull from. If unspecified, it will default to one of the DCs which contains the highest available store-version.
kafka.input.fabric = dc-name
To specify the address of the Kafka cluster to pull from. If unspecified, it will use the one returned by the controller of the datacenter chosen to pull from.
kafka.input.broker.url = kafka-url:port
To specify the size of the offset range each mapper task will be responsible for. Each Kafka partition is fetched in parallel by multiple mappers, each taking care of distinct ranges. If unspecified, the default is 5M.
kafka.input.max.records.per.mapper = 1000000
To specify whether to enable the Combiner of the Map Reduce job, in order to prune the shuffling traffic between mappers and reducers. This is an experimental functionality that has not been vetted for production usage yet. If unspecified, it defaults to false.
kafka.input.combiner.enabled = true
To specify whether to enable TTL for the repush job. This is an experimental functionality. If unspecified, it defaults to false.
repush.ttl.enable = true
One potential enhancement would be to add support for changing the compression setting of a store, which currently requires a Full Push, and which Repush does not support yet.