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With state-machine replication, every update transaction must be executed by all servers.
If t is an update transaction, the consistency of the values read by t are performed by the certification test.
When the i-th update transaction t commits on s, the value, version, and digest of each data item x written by t are updated.
We extend the argument presented in the previous section to show that all of t's read commands can be placed in between two update transactions, namely, after the transaction u that created the first item read by t and before any other update transaction.
File names and file paths are each limited to a 32 KB Unicode text string.allocation-on-write update strategy for metadata, which allocates new chunks for every update transaction and uses large IO batches.
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This asynchronous scheme does not add communication overhead to update transactions.
With the approach presented in this chapter, clients submit query or update transactions to this middleware, instead of directly communicating with specific cluster nodes.
With primary-backup replication, the primary first executes update transactions and then propagates the database changes to the backups, which apply them without reexecuting the transactions.
Sharding – either manually at application level or automated by distributed storage frameworks – can allow scaling out by adding more servers, but it also leads to application complexity if queries and update transactions span over several shards of data.
It contains read as well as update transactions.
While data items are being broadcast, update transactions may install new values for them.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com