Abstract: A water permeable court surface comprises a porous substrate (5) on which there is a porous rubber shock layer (3) on which is at least one layer (1,2) of an acrylic composition, wherein the acrylic composition comprises a two pack water based polyurethane.

Abstract: Resource provisioning systems and methods are described. In an embodiment, a system includes a plurality of shared storage devices collectively storing database data, an execution platform, and a compute service manager. The compute service manager is configured to determine a task to be executed in response to a trigger event and determine a query plan for executing the task, wherein the query plan comprises a plurality of discrete subtasks. The compute service manager is further configured to assign the plurality of discrete subtasks to one or more nodes of a plurality of nodes of the execution platform, determine whether execution of the task is complete, and in response to determining the execution of the task is complete, store a record in the plurality of shared storage devices indicating the task was completed.

Abstract: The subject technology inserts, by a first transaction, a new version of an object, the first transaction including a first statement to perform an update operation to a row in a first table, the object corresponding to data in the row to be updated, the first statement including information comprising an object key associated with the object. The subject technology performs, by a second transaction, a range read, the range read including information indicating the object key. The subject technology receives a set of conflicting transactions from the range read. The subject technology determines that a conflict occurred between the first transaction and a third transaction from the set of conflicting transactions. The subject technology performs a restart of the first transaction in response to determining that the conflict occurred.

Abstract: The subject technology generates, by a compute service manager, a schema hash value for a new schema version associated with a new schema version value, the schema hash value based on determining a sum of hash values of a set of attributes of value columns, the set of attributes comprises a column identifier, and a logical type of a column. The subject technology stores a mapping of the schema hash value to the new schema version value for a table in a metadata database. The subject technology stores a new schema entry based on the schema hash value, the new schema version value, and a new column for the table in the metadata database, the metadata database storing multiple entries for different schema versions, each entry including a particular schema hash value for mapping to a corresponding schema version from the different schema versions.

Abstract: The subject technology serializes, by at least one hardware processor, non-primary key data of column-organized data into compressed serialized value data that is in a row-organized sequence, the compressed serialized value data compressed using at least one bitmap, the non-primary key data comprising a schema identifier, the column-organized data being stored in a columnar database system, the column-organized data comprising primary key data and the non-primary key data. The subject technology stores the compressed serialized value data in a key-value data store of a key-value database system, the key-value database system processing key-value data in a key-value format. The subject technology receives a query by the columnar database system. The subject technology deserializes a portion of the compressed serialized value data that corresponds to the query. The subject technology processes the query using the columnar database system.

Abstract: The subject technology performs a locking operation on a first set of keys by a first statement of a first transaction. The subject technology determines that a conflict occurred between the first statement and a second transaction. The subject technology determines that the second transaction has yet to complete after a predetermined period of time. The subject technology performs a deadlock detection process where the subject technology stores a key and value in a table indicating the first transaction and the second transaction, detects, based at least in part on a graph traversal of the table starting from the first transaction, a cycle between the first transaction and the second transaction, and determines that the first transaction is a youngest transaction in the detected cycle. The subject technology ceases execution of the first transaction in response to the first transaction being a youngest transaction in a detected cycle.

Abstract: A distributed database system can implement a column-based database system and a row-based database system for processing data. The row-based database system can store data organized into key value pairs, and data to be processed by the row-based database system is converted to a key-value format compressing keys that correspond to values. The distributed database system can perform serialization and compression in converting the data to the key-value format for efficient data storage performance. The distributed database system can unpack portions of the converted serialized compressed data in response to queries that process a portion of serialized compressed data without unpacking the entire converted dataset.

Abstract: The subject technology performs a read operation from a first transaction on a table in a database. The subject technology determines a first commit version identifier corresponding to first data resulting from the read operation. The subject technology proceeds to perform a particular write operation from the first transaction. The subject technology determines that the first commit version identifier fails to match a second commit version identifier corresponding to a second transaction. The subject technology aborts the particular write operation from the first transaction. The subject technology performs a particular read operation from the first transaction. The subject technology determines a particular commit version identifier corresponding to particular data resulting from the particular read operation. The subject technology retries to perform the particular write operation.

Abstract: The subject technology receives a query, the query including a set of statements for performing the query. The subject technology populates a compilation context based at least in part the query. The subject technology invokes a compiler to perform a compilation process based on the compilation context. The subject technology performs a lookup operation on a stored plan cache for an exact match based on information from the compilation context. The subject technology, in response to determining an exact match, determines whether the particular query plan requires re-compilation based on a data dependent optimization. The subject technology determines whether a plan cache entry corresponding to the particular query plan includes a data property constraint. The subject technology determines whether the data property constraint still holds based on a set of data properties.

Abstract: The subject technology performs, using asynchronous threads of execution nodes of a distributed database, asynchronous transformation and compaction of key-value pairs of data that correspond to a plurality of transactional queries, the asynchronous threads transforming the key-value pairs from an initial format to a commit format that includes a commit time of data committed to the distributed database, the asynchronous threads compacting the key-value pairs by deleting the key-value pairs in the initial format. The subject technology processes, using transactional threads in the execution nodes, the plurality of transactional queries to generate results data, the asynchronous threads performing the asynchronous transformation and compaction while the transactional threads generate the results data. The subject technology stores the results data.

Abstract: The subject technology determines that a first visible version of a key is not one of a N number of newest versions of the key based on searching for the key in a regular space to locate the first visible version of the key. The subject technology performs a search of an undo space to locate a second visible version of the key. The subject technology determines whether the first visible version or the second visible version of the key is newer. The subject technology provides a newer version of the key between the first visible version and the second visible version of the key.

Abstract: The subject technology receives, by a first set of decoupled compute instances of a distributed database, a plurality of transactional queries against a transactional processing database stored on a first set of decoupled storage instances, at least one of the first set of decoupled compute instances bypassing a compiler of the distributed database to process the plurality of transactional queries using locally cached data. The subject technology receives at least one select query for processing on an aggregated analytical query database of the distributed database. The subject technology generates, by the compiler of the distributed database, a query plan for execution of the at least one select query using a second set of decoupled compute instances. The subject technology generates, by the second set of decoupled compute instances, an aggregated analytical query result dataset for the at least one select query according to the query plan.

Abstract: The subject technology performs a search for a key in a regular space to locate a first visible version of the key. The subject technology determines that the first visible version of the key is not one of a N number of newest versions of the key. The subject technology performs a search of an undo space to locate a second visible version of the key. The subject technology determines whether the first visible version or the second visible version of the key is newer. The subject technology provides a newer version of the key between the first visible version and the second visible version of the key.

Abstract: The subject technology receives a statement to perform an operation to add a new column into a table. The subject technology generates a schema hash value for a new schema version associated with a new schema version value. The subject technology stores a mapping of the schema hash value to the new schema version value for the table in a metadata database. The subject technology stores a new schema entry based on the schema hash value, the new schema version value, and the new column for the table in the metadata database. The subject technology performs an operation to add the new column to the table.

Abstract: A distributed database compaction system can perform compaction for transactional queries in an asynchronous matter without affecting completion of the queries. The compaction system can implement asynchronous transformation of key pairs in the database, and older keys can be periodically purged using a scheduled compactor. Subsequent queries use the compacted stored keys to perform efficient queries with direct reads of committed transactions and more efficient access to key values stores of the distributed database.

Abstract: The subject technology receives a first transaction, the first transaction to be executed on linearizable storage. The subject technology assigns, prior to performing a read operation, a first read version to the first transaction, the first read version indicating a first version of the linearizable storage, the first read version corresponding to a particular timestamp of a current time. The subject technology performs the read operation from the first transaction on a table in a database. The subject technology determines a first commit version identifier corresponding to first data resulting from the read operation. The subject technology proceeds to perform a particular write operation from the first transaction. The subject technology determines that the first commit version identifier fails to match a second commit version identifier corresponding to a second transaction. The subject technology aborts the particular write operation from the first transaction.

Abstract: The subject technology performs a search for a key in a regular space to locate a first visible version of the key. The subject technology determines that the first visible version of the key is not one of a N number of newest versions of the key. The subject technology performs a search of an undo space to locate a second visible version of the key. The subject technology determines whether the first visible version or the second visible version of the key is newer. The subject technology provides a newer version of the key between the first visible version and the second visible version of the key.

Abstract: The subject technology receives a key-value pair. The subject technology determines a value size of the key-value pair is greater than a threshold size for an out of line blob (OLB) key space. The subject technology performs a write operation of a key-pointer pair into a regular space. The subject technology performs a plurality of write operations for multiple key-value parts of the key-value pair into the OLB key space. The subject technology performs conflict checking of the regular space for the key-value pair.

Abstract: A hybrid network-based database system for handling OLTP and OLAP queries using decoupled compute and storage devices. A set of decoupled compute instances perform transactions on an OLTP database, and the data is replicated to an OLAP database, which is managed by another set of decoupled compute instances. Further, in response to queries, the database system can retrieve data from the OLTP and OLAP database for merging and processing according to the query.

Abstract: A distributed database system can implement a column-based database system and a row-based database system for processing data. The row-based database system can store data organized into key value pairs, and data to be processed by the row-based database system is converted to a key-value format compressing keys that correspond to values. The distributed database system can perform serialization and compression in converting the data to the key-value format for efficient data storage performance. The distributed database system can unpack portions of the converted serialized compressed data in response to queries that process a portion of serialized compressed data without unpacking the entire converted dataset.