Abstract: A query optimizer may receive a query block that includes a hint. The hint may include a unique object identifier to specify an object to which the hint applies. In some instances, the object may correspond to a nested query block. A query optimization plan may be determined based, at least in part, on the hint. The query execution plan may then be executed to query a data store.

Abstract: The disclosure is directed to data replication in a distributed computing system. The system ensures a read-after-write consistency for the replicated data by associating the write requests with unique version numbers, and using them to obtain the data. A server associates a write request with a unique version number, and returns the version number to a client, which can use it in a subsequent read request for the data. On receiving a read request for a first data, the server extracts the version number from the request and ensures a specified write request associated with the version number is processed prior to obtaining the data. If the specified write request is processed, the server obtains the data and returns it to the client, else the server processes the pending write requests up until the version number and then the specified write request, and returns the specified data to the client.

Abstract: The disclosure is directed to data replication in a distributed computing system. The data, e.g., received from a client, is replicated to a first set of servers (“sync replica set”) synchronously and to a second set of servers asynchronously (“async tree”). A server can be a primary, secondary or a follower server. A sync replica set includes a primary server and one or more secondary servers. The async tree includes multiple follower servers deployed in a tree structure. A primary server can replicate the data to the secondary servers synchronously, and the secondary servers can replicate the data to one or more follower servers, e.g., a root node of the async tree, asynchronously. The root node then replicates the data to children of the root node, which then replicate to their children and so on until the leaf nodes of the async tree receive the data.

Abstract: The disclosure is directed to data replication in a distributed computing system. The system ensures a read-after-write consistency for the replicated data by associating the write requests with unique version numbers, and using them to obtain the data. A server associates a write request with a unique version number, and returns the version number to a client, which can use it in a subsequent read request for the data. On receiving a read request for a first data, the server extracts the version number from the request and ensures a specified write request associated with the version number is processed prior to obtaining the data. If the specified write request is processed, the server obtains the data and returns it to the client, else the server processes the pending write requests up until the version number and then the specified write request, and returns the specified data to the client.

Abstract: The disclosure is directed to data replication in a distributed computing system. The data, e.g., received from a client, is replicated to a first set of servers (“sync replica set”) synchronously and to a second set of servers asynchronously (“async tree”). A server can be a primary, secondary or a follower server. A sync replica set includes a primary server and one or more secondary servers. The async tree includes multiple follower servers deployed in a tree structure. A primary server can replicate the data to the secondary servers synchronously, and the secondary servers can replicate the data to one or more follower servers, e.g., a root node of the async tree, asynchronously. The root node then replicates the data to children of the root node, which then replicate to their children and so on until the leaf nodes of the async tree receive the data.

Abstract: A distributed data warehouse system may maintain data blocks on behalf of clients in multiple clusters in a data store. Each cluster may include a single leader node and multiple compute nodes, each including multiple disks storing data. The warehouse system may store primary and secondary copies of each data block on different disks or nodes in a cluster. Each node may include a data structure that maintains metadata about each data block stored on the node, including its unique identifier. The warehouse system may back up data blocks in a remote key-value backup storage system with high durability. A streaming restore operation may be used to retrieve data blocks from backup storage using their unique identifiers as keys. The warehouse system may service incoming queries (and may satisfy some queries by retrieving data from backup storage on an as-needed basis) prior to completion of the restore operation.

Abstract: A data warehousing system maintains large tables comprising a significant quantity of historical data. In general, the addition of small quantities of additional data is not likely to significantly alter the accuracy of statistics relied upon by a query optimizer. However, certain statistics may have an influence on optimizer behavior, even though the size of the additional data is small. Certain statistics relied on by the optimizer may be incrementally updated based at least in part on characteristics of the additional data without requiring a scan of the preexisting data.