Transactional IO Path
Review the Distributed ACID Transactions section for an overview of some common concepts used in YugaByte DB’s implementation of distributed transactions. In this section, we will go over the write path of a transaction modifying multiple keys, and the read path for reading a consistent combination of values from multiple tablets.
Let us walk through the lifecycle of a single distributed write-only transaction. Suppose we are
trying to modify rows with keys
k2. If they belong to the same tablet, we could execute
this transaction as a single-shard transaction, in which
case atomicity would be ensured by the fact that both updates would be replicated as part of the
same Raft log record. However, in the most general case, these keys would belong to different
tablets, and that is what we’ll assume from this point on.
The diagram below shows the high-level steps of a distributed write-only transaction, not including any conflict resolution.
1. Client’s request
The client sends a request to a YB tablet server that requires a distributed transaction. We currently only support transactions that can be expressed by a single client request. Here is an example using our extension to CQL:
START TRANSACTION; UPDATE t1 SET v = 'v1' WHERE k = 'k1'; UPDATE t2 SET v = 'v2' WHERE k = 'k2'; COMMIT;
The tablet server that receives the transactional write request becomes responsible for driving all the steps involved in this transaction, as described below. This orchestration of transaction steps is performed by a component we call a transaction manager. Every transaction is handled by exactly one transaction manager.
2. Creating a transaction record
We assign a transaction id and select a transaction status tablet that would keep track of a transaction status record that has the following fields:
- Status (pending, committed, or aborted)
- Commit hybrid timestamp, if committed
- List of ids of participating tablets, if committed
It makes sense to select a transaction status tablet in a way such that the transaction manager’s tablet server is also the leader of its Raft group, because this allows to cut the RPC latency on querying and updating the transaction status. But in the most general case, the transaction status tablet might not be hosted on the same tablet server that initiates the transaction.
3. Writing provisional records
We start writing provisional records to tablets containing the rows we need to modify. These provisional records contain the transaction id, the values we are trying to write, and the provisional hybrid timestamp. This provisional hybrid timestamp is not the final commit timestamp, and will in general be different for different provisional records within the same transaction. In contranst, there is only one commit hybrid timestamp for the entire transaction.
As we write the provisional records, we might encounter conflicts with other transactions. In this case we would have to abort and restart the transaction. These restarts still happen transparently to the client up to a certain number of retries.
4. Committing the transaction
When the transaction manager has written all the provisional records, it commits the transaction by sending an RPC request to the transaction status tablet. The commit operation will only succeed if the transaction has not yet been aborted due to conflicts. The atomicity and durability of the commit operation is guaranteed by the transaction status tablet’s Raft group. Once the commit operation is complete, all provisional records immediately become visible to clients.
The commit request the transaction manager sends to the status tablet includes the list of tablet ids of all tablets that participate in the transaction. Clearly, no new tablets can be added to this set by this point. The status tablet needs this information to orchestrate cleaning up provisional records in participating tablets.
5. Sending the response back to client
The YQL engine sends the response back to the client. If any client (either the same one or different) sends a read request for the keys that were written, the new values are guaranteed to be reflected in the response, because the transaction is already committed. This property of a database is sometimes called the “read your own writes” guarantee.
6. Asynchronously applying and cleaning up provisional records
This step is coordinated by the transaction status tablet after it receives the commit message for our transaction and successfully replicates a change to the transaction’s status in its Raft group. The transaction status tablet already knows what tablets are participating in this transaction, so it sends cleanup requests to them. Each participating tablet records a special “apply” record into its Raft log, containing the transaction id and commit timestamp. When this record is Raft-replicated in the participating tablet, the tablet remove the provisional records belonging to the transaction, and writes regular records with the correct commit timestamp to its RocksDB databases. These records now are virtually indistinguishable from those written by regular single-row operations.
Once all participating tablets have successfully processed these “apply” requests, the status tablet can delete the transaction status record, because all replicas of participating tablets that have not yet cleaned up provisional records (e.g. slow followers) will do so based on information available locally within those tablets. The deletion of the status record happens by writing a special “applied everywhere” entry to the Raft log of the status tablet. Raft log entries belonging to this transaction will be cleaned up from the status tablet’s Raft log as part of regular garbage-collection of old Raft logs soon after this point.
YugaByte DB is an MVCC database, which means it internally keeps track of multiple versions of the same value. Read operations don’t take any locks, and rely on the MVCC timestamp in order to read a consistent snapshot of the data. A long-running read operation, either single-shard or cross-shard, can proceed concurrently with write operations modifying the same key.
In the ACID Transactions section, we talked about how up-to-date reads are performed from a single shard (tablet). In that case, the most recent value of a key is simply the value written by the last committed Raft log record that the Raft leader knows about. For reading multiple keys from different tablets, though, we have to make sure that the values we read come from a recent consistent snapshot of the database. Here is a clarification of these two properties of the snapshot we have to choose:
Consistent snapshot. The snapshot must show any transaction’s records fully, or not show them at all. It cannot contain half of the values written by a transaction and omit the other half. We ensure the snapshot is consistent by performing all reads at a particular hybrid time (we will call it ht_read), and ignoring any records with higher hybrid time.
Recent snapshot. The snapshot includes any value that any client might have already seen, which means all values that were written or read before this read operation was initiated. This also includes all previously written values that other components of the client application might have written to or read from the database. The client performing the current read might rely on presence of those values in the result set, because those other components of the client application might have communicated this data to the current client through asynchronous communication channels. We ensure the snapshot is recent by restarting the read operation when it is determined that the chosen hybird time was too low, i.e. that there are some records that could have been written before the read operation was initiated but have a hybrid time higher than the currently chosen ht_read.
1. Client’s request handling and read transaction initialization
The client’s request to either the YCQL or YEDIS or YSQL API arrives at the YQL engine of a tablet server. The YQL engine detects that the query requests rows from multiple tablets and starts a read-only transaction. A hybrid time ht_read is selected for the request, which could be either the current hybrid time on the YQL engine’s tablet server, or the safe time on one of the involved tablets. The latter case would reduce waiting for safe time for at least that tablet and is therefore better for performance. Typically, due to our load-balancing policy, the YQL engine receiving the request will also host some of the tablets that the request is reading, allowing to implement the more performant second option without an additional RPC round-trip.
We also select a point in time we call global_limit, computed as physical_time + max_clock_skew, which allows us to determine whether a particular record was written definitely after our read request started. max_clock_skew is a globally configured bound on clock skew between different YugaByte DB servers. (We’ve also designed an adaptive clock skew tracking algorithm that allows to avoid the need to specify a global clock skew bound, which is part of YugaByte DB Enterprise Edition).
2. Read from all tablets at the chosen hybrid time
The YQL engine sends requests to all tablets the transaction needs to read from. Each tablet waits for ht_read to become a safe time to read at according to our definition of safe time, and then starts executing its part of the read request from its local DocDB.
WHen a tablet server sees a relevant record with a hybrid time ht_record, it executes the following logic:
- If ht_record ≤ ht_read, include the record in the result.
- If ht_record > definitely_future_ht, exclude the record from the result. The meaning of definitely_future_ht is that it it is a hybrid time such that a record with a higher hybrid time than that was definitely written after our read request started. You can assume definitely_future_ht above to simply be global_limit for now. We will clarify how exactly it is computed in a moment.
- If ht_read < ht_record ≤ definitely_future_ht, we don’t know if this record was written before or after our read request started. But we can’t just omit it from the result, because if it was in fact written before the read request, this may produce a client-observed inconsistency. Therefore, we have to restart the entire read operation with an updated value of ht_read = ht_record.
To prevent an infinite loop of these read restarts, we also return a tablet-dependent hybrid time value local_limittablet to the YQL engine, computed as the current safe time in this tablet. We now know that any record (regular or provisional) written to this tablet with a hybrid time higher than local_limittablet could not have possibly been written before our read request started. Therefore, we won’t have to restart the read transaction if we see a record with a hybrid time higher than local_limittablet in a later attempt to read from this tablet within the same transaction, and we set definitely_future_ht = min(global_limit, local_limittablet) on future attempts.
3. Tablets query the transaction status
As each participating tablet reads from its local DocDB, it might encounter provisional records for which it does not yet know the final transaction status and commit time. In these cases, it would send a transaction status request to the transaction status tablet. If a transaction is committed, it is treated as if DocDB already contained permanent records with hybrid time equal to the transaction’s commit time. The cleanup of provisional records happens independently and asynchronously.
4. Tablets respond to YQL
Each tablet’s response to YQL contains the following:
- Whether or not read restart is required.
- local_limittablet to be used to restrict future read restarts caused by this tablet.
- The actual values that have been read from this tablet.
5. YQL sends the response to the client
As soon as all read operations from all participating tablets succeed and it has been determined that there is no need to restart the read transaction, a response is sent to the client using the appropriate wire protocol.
See the Distributed ACID Transactions section to review some common concepts relevant to YugaByte DB’s implementation of distributed transactions.