- Consensus algorithm
- Key-value engine
- Distributed transaction
- Remote Procedure Calls (RPC)
- Resource scheduling
- Distributed SQL over TiKV
The timestamp oracle plays a significant role in the Percolator Transaction model, it is a server that hands out timestamps in strictly increasing order, a property required for correct operation of the snapshot isolation protocol.
Since every transaction requires contacting the timestamp oracle twice, this service must scale well. The timestamp oracle periodically allocates a range of timestamps by writing the highest allocated timestamp to stable storage; then with that allocated range of timestamps, it can satisfy future requests strictly from memory. If the timestamp oracle restarts, the timestamps will jump forward to the maximum allocated timestamp. Timestamps never go “backwards”.
To save RPC overhead (at the cost of increasing transaction latency) each timestamp requester batches timestamp requests across transactions by maintaining only one pending RPC to the oracle. As the oracle becomes more loaded, the batching naturally increases to compensate. Batching increases the scalability of the oracle but does not affect the timestamp guarantees.
The transaction protocol uses strictly increasing timestamps to guarantee that Get() returns all committed writes before the transaction’s start timestamp. To see how it provides this guarantee, consider a transaction R reading at timestamp TR and a transaction W that committed at timestamp TW < TR; we will show that R sees W’s writes. Since TW < TR, we know that the timestamp oracle gave out TW before or in the same batch as TR; hence, W requested TW before R received TR. We know that R can’t do reads before receiving its start timestamp TR and that W wrote locks before requesting its commit timestamp TW . Therefore, the above property guarantees that W must have at least written all its locks before R did any reads; R’s Get() will see either the fully committed write record or the lock, in which case W will block until the lock is released. Either way, W’s write is visible to R’s Get().
In our system, the timestamp oracle has been embeded into Placement Driver (PD). PD is the management component with a “God view” and is responsible for storing metadata and conducting load balancing.
Practice in TiKV
We use batching and preallocating techniques to increase the timestamp oracle’s throughput, and also we use a Raft group to tolerate node failure, but there are still some disadvantages to allocating timestamps from a single node. One disadvantage is that the timestamp oracle can’t be scaled to multiple nodes. Another is that when the current Raft leader fails, there is a gap wherein the system cannot allocate a timestamp before a new leader has been elected. Finally, when the timestamp requestor is located at a remote datacenter the requestor must tolerate the high latency caused by the network round trip. There are some potential solutions for this final case, such as Google Spanner’s TrueTime mechanism and HLCs (Hybrid Logical Clocks).