Abstract: Techniques described herein can allow users to share cached results of an original query with other users while protecting sensitive information. The techniques described herein can check whether the other users have access to the underlying data queried before allowing those users to see the stored query results. That is, the system may perform privilege checks on the shared users before giving them access to the stored query results but without having to re-run the original query.

Abstract: Techniques described herein can allow users to share cached results of an original query with other users while protecting sensitive information. The techniques described herein can check whether the other users have access to the underlying data queried before allowing those users to see the stored query results. That is, the system may perform privilege checks on the shared users before giving them access to the stored query results but without having to re-run the original query.

Abstract: Techniques for making machine learning inference calls for database query processing are described. In some embodiments, a method of making machine learning inference calls for database query processing may include generating a first batch of machine learning requests based at least on a query to be performed on data stored in a database service, wherein the query identifies a machine learning service, sending the first batch of machine learning requests to an input buffer of an asynchronous request handler, the asynchronous request handler to generate a second batch of machine learning requests based on the first batch of machine learning requests, and obtaining a plurality of machine learning responses from an output buffer of the asynchronous request handler, the machine learning responses generated by the machine learning service using a machine learning model in response to receiving the second batch of machine learning requests.

Abstract: Techniques described herein can allow users to share cached results of an original query with other users while protecting sensitive information. The techniques described herein can check whether the other users have access to the underlying data queried before allowing those users to see the stored query results. That is, the system may perform privilege checks on the shared users before giving them access to the stored query results but without having to re-run the original query.

Abstract: Techniques described herein can allow users to share cached results of an original query with other users while protecting sensitive information. The techniques described herein can check whether the other users have access to the underlying data queried before allowing those users to see the stored query results. That is, the system may perform privilege checks on the shared users before giving them access to the stored query results but without having to re-run the original query.

Abstract: Techniques for making machine learning inference calls for database query processing are described. In some embodiments, a method of making machine learning inference calls for database query processing may include generating a first batch of machine learning requests based at least on a query to be performed on data stored in a database service, wherein the query identifies a machine learning service, sending the first batch of machine learning requests to an input buffer of an asynchronous request handler, the asynchronous request handler to generate a second batch of machine learning requests based on the first batch of machine learning requests, and obtaining a plurality of machine learning responses from an output buffer of the asynchronous request handler, the machine learning responses generated by the machine learning service using a machine learning model in response to receiving the second batch of machine learning requests.

Abstract: Techniques described herein can allow users to share cached results of an original query with other users while protecting sensitive information. The techniques described herein can check whether the other users have access to the underlying data queried before allowing those users to see the stored query results. That is, the system may perform privilege checks on the shared users before giving them access to the stored query results but without having to re-run the original query.

Abstract: Technologies are described for restarting an application while maintaining data in memory (e.g., using shared memory). For example, shared memory can be associated with an application. The shared memory can also be associated with a holder process to maintain the shared memory from the time the application stops to the time the application starts again. When the application starts, the shared memory can be associated with the started application. In addition, restart of in-memory databases can be provided using shared memory. For example, in-memory data can be maintained when a database process or database management system stops and starts (e.g., during a restart).

Abstract: Techniques for making machine learning inference calls for database query processing are described. In some embodiments, a method of making machine learning inference calls for database query processing may include generating a first batch of machine learning requests based at least on a query to be performed on data stored in a database service, wherein the query identifies a machine learning service, sending the first batch of machine learning requests to an input buffer of an asynchronous request handler, the asynchronous request handler to generate a second batch of machine learning requests based on the first batch of machine learning requests, and obtaining a plurality of machine learning responses from an output buffer of the asynchronous request handler, the machine learning responses generated by the machine learning service using a machine learning model in response to receiving the second batch of machine learning requests.

Abstract: A method can include: generating a query execution plan for a query including a plurality of operations that operate on data from a relational database and data from a non-structured query language (NoSQL) database, the generating comprising optimizing the query by: identifying a first operation that operate on data from the relational database and an intermediate result output by a second operation, the second operation outputting the intermediate result by operating on the data from the NoSQL database; and determining a fixed datatype for the intermediate result, an indication of the fixed datatype being included in the query execution plan; and executing, based on the query execution plan, the query, the executing of the query comprising converting the intermediate result to the fixed datatype, the converting enabling the first operation to operate on the intermediate result output by the second operation along with the data from the relational database.

Abstract: A system, computer-implemented method, and computer-program product embodiments for rebuilding database indexes is disclosed. A pre-loader thread rebuilds a plurality of indexes in the background once a database is initialized. Simultaneously, a query processor receives queries from a user/client of a database. If an index that is required by the query processor has not yet been rebuilt by the pre-loader, a request to rebuild the index is sent to an asynchronous index loader. The asynchronous index loader places the request in a queue, processes the queue based on available system resources, and then rebuilds the requested indexes. Once the necessary indexes have been rebuilt, a wake-up signal is sent to the query processor which then proceeds to execute the query from a user/client.

Abstract: Technologies are provided for using a multi-user execution plan cache to process database queries. A database query processor can be configured to store execution plans in a multi-user execution plan cache. The query processor can determine whether an execution plan is shareable by multiple database users. If the execution plan is shareable, it can be stored in the cache in association with a sharing user identifier. When a database query is received, the query processor can determine that the query can be performed using the cached execution plan. If the cached execution plan is shareable, the database query can determine whether the cached execution plan is valid for a database user associated with the received database query. If the cached execution plan is valid for the database user, the query processor uses the cached execution plan to perform the query for the associated database user.

Abstract: Technologies are described for restarting an application while maintaining data in memory (e.g., using shared memory). For example, shared memory can be associated with an application. The shared memory can also be associated with a holder process to maintain the shared memory from the time the application stops to the time the application starts again. When the application starts, the shared memory can be associated with the started application. In addition, restart of in-memory databases can be provided using shared memory. For example, in-memory data can be maintained when a database process or database management system stops and starts (e.g., during a restart).

Abstract: Technologies are described for restarting an application while maintaining data in memory (e.g., using shared memory). For example, shared memory can be associated with an application. The shared memory can also be associated with a holder process to maintain the shared memory from the time the application stops to the time the application starts again. When the application starts, the shared memory can be associated with the started application. In addition, restart of in-memory databases can be provided using shared memory. For example, in-memory data can be maintained when a database process or database management system stops and starts (e.g., during a restart).

Abstract: Technologies for performing garbage collection in database systems, such as multi-version concurrency control (MVCC) database systems, are described. For example, different garbage collection techniques can be used separately or in various combinations, including interval garbage collection, group garbage collection, table garbage collection, and combinations. For example, a particular type of combination, called hybrid garbage collection, uses technique from interval garbage collection and group garbage collection, or from interval, group, and table garbage collection.

Abstract: Technologies for performing garbage collection in database systems, such as multi-version concurrency control (MVCC) database systems, are described. For example, different garbage collection techniques can be used separately or in various combinations, including interval garbage collection, group garbage collection, table garbage collection, and combinations. For example, a particular type of combination, called hybrid garbage collection, uses technique from interval garbage collection and group garbage collection, or from interval, group, and table garbage collection.

Abstract: Technologies are provided for using a multi-user execution plan cache to process database queries. A database query processor can be configured to store execution plans in a multi-user execution plan cache. The query processor can determine whether an execution plan is shareable by multiple database users. If the execution plan is shareable, it can be stored in the cache in association with a sharing user identifier. When a database query is received, the query processor can determine that the query can be performed using the cached execution plan. If the cached execution plan is shareable, the database query can determine whether the cached execution plan is valid for a database user associated with the received database query. If the cached execution plan is valid for the database user, the query processor uses the cached execution plan to perform the query for the associated database user.

Abstract: Technologies are described for performing proactive memory allocation (e.g., pre-allocation). Proactive memory allocation (e.g., proactive memory page allocation) can be provided for applications, such as database applications. For example, an application can be associated with a free memory pool (e.g., a free memory pool containing free memory pages). A dedicated thread of the application can monitor the free memory pool and perform proactive memory allocation when needed (e.g., when the amount of memory in the pool is low). For example, the dedicated thread can obtain new free memory from the operating system and add it to the pool.

Abstract: Technologies for performing garbage collection in database systems, such as multi-version concurrency control (MVCC) database systems, are described. For example, different garbage collection techniques can be used separately or in various combinations, including interval garbage collection, group garbage collection, table garbage collection, and combinations. For example, a particular type of combination, called hybrid garbage collection, uses technique from interval garbage collection and group garbage collection, or from interval, group, and table garbage collection.

Abstract: A method can include: generating a query execution plan for a query including a plurality of operations that operate on data from a relational database and data from a non-structured query language (NoSQL) database, the generating comprising optimizing the query by: identifying a first operation that operate on data from the relational database and an intermediate result output by a second operation, the second operation outputting the intermediate result by operating on the data from the NoSQL database; and determining a fixed datatype for the intermediate result, an indication of the fixed datatype being included in the query execution plan; and executing, based on the query execution plan, the query, the executing of the query comprising converting the intermediate result to the fixed datatype, the converting enabling the first operation to operate on the intermediate result output by the second operation along with the data from the relational database.