Computer data system data source refreshing using an update propagation graph

• US 10,572,474 B2
• Filed: 09/20/2017
• Issued: 02/25/2020
• Est. Priority Date: 05/14/2015
• Status: Active Grant

• Current Assignee: Deephaven Data Labs, LLC
• Original Assignee: Deephaven Data Labs, LLC
• Inventors: Wright, Charles, Caudy, Ryan, Kent, IV, David R., Bronnimann, Herve, Teodorescu, Radu
• Primary Examiner(s): Chbouki, Tarek
• Application Number:
  US15/710,513
• Publication Number:
  US 20180011891A1
• Time in Patent Office: 888 Days
• CPC Class Codes: G06F 11/1451 by selection of backup contents
• CPC Class Codes: G06F 11/1464 for networked environments
• CPC Class Codes: G06F 11/1469 Backup restoration techniques
• CPC Class Codes: G06F 12/0261 using reference counting
• CPC Class Codes: G06F 12/084 with a shared cache
• CPC Class Codes: G06F 12/1483 using an access-table, e.g. matrix or list
• CPC Class Codes: G06F 15/17331 Distributed shared memory [DSM], e.g. remote direct memory access [RDMA]
• CPC Class Codes: G06F 16/113 Details of archiving lifecycle management in storage systems G06F3/0649; backup systems G06F11/1446
• CPC Class Codes: G06F 16/144 Query formulation
• CPC Class Codes: G06F 16/162 Delete operations erasing in storage systems G06F3/0652
• CPC Class Codes: G06F 16/215 Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/221 Column-oriented storage; Management thereof
• CPC Class Codes: G06F 16/2228 Indexing structures
• CPC Class Codes: G06F 16/2237 Vectors, bitmaps or matrices
• CPC Class Codes: G06F 16/2246 Trees, e.g. B+trees
• CPC Class Codes: G06F 16/2255 Hash tables
• CPC Class Codes: G06F 16/2264 Multidimensional index structures
• CPC Class Codes: G06F 16/2272 Management thereof
• CPC Class Codes: G06F 16/2282 Tablespace storage structures; Management thereof
• CPC Class Codes: G06F 16/2291 : User-Defined Types; Storage management thereof
• CPC Class Codes: G06F 16/23 : Updating
• CPC Class Codes: G06F 16/2308 : Concurrency control transaction processing G06F9/466
• CPC Class Codes: G06F 16/2358 : Change logging, detection, and notification replication G06F16/27
• CPC Class Codes: G06F 16/2365 : Ensuring data consistency and integrity
• CPC Class Codes: G06F 16/2372 : Updates performed during offline database operations
• CPC Class Codes: G06F 16/2379 : Updates performed during online database operations; commit processing
• CPC Class Codes: G06F 16/242 : Query formulation
• CPC Class Codes: G06F 16/2428 : Query predicate definition using graphical user interfaces, including menus and forms G06F16/2423 takes precedence
• CPC Class Codes: G06F 16/245 : Query processing
• CPC Class Codes: G06F 16/2453 : Query optimisation
• CPC Class Codes: G06F 16/24534 : Query rewriting; Transformation
• CPC Class Codes: G06F 16/24535 : of sub-queries or views
• CPC Class Codes: G06F 16/24537 : of operators
• CPC Class Codes: G06F 16/24539 : using cached or materialised query results
• CPC Class Codes: G06F 16/2455 : Query execution
• CPC Class Codes: G06F 16/24553 : of query operations
• CPC Class Codes: G06F 16/2456 : Join operations
• CPC Class Codes: G06F 16/24561 : Intermediate data storage techniques for performance improvement
• CPC Class Codes: G06F 16/2457 : with adaptation to user needs
• CPC Class Codes: G06F 16/24575 : using context
• CPC Class Codes: G06F 16/248 : Presentation of query results
• CPC Class Codes: G06F 16/254 : Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/27 : Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
• CPC Class Codes: G06F 16/278 : Data partitioning, e.g. horizontal or vertical partitioning
• CPC Class Codes: G06F 16/285 : Clustering or classification
• CPC Class Codes: G06F 16/9024 : Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/907 : Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/951 : Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 : Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 : Presentation of query results
• CPC Class Codes: G06F 16/9566 : URL specific, e.g. using aliases, detecting broken or misspelled links
• CPC Class Codes: G06F 16/9574 : of access to content, e.g. by caching
• CPC Class Codes: G06F 17/40 : Data acquisition and logging for input to computer G06F3/00
• CPC Class Codes: G06F 21/00 : Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
• CPC Class Codes: G06F 21/6209 : to a single file or object, e.g. in a secure envelope, encrypted and accessed using a key, or with access control rules appended to the object itself
• CPC Class Codes: G06F 2201/805 : Real-time
• CPC Class Codes: G06F 2201/84 : Using snapshots, i.e. a logical point-in-time copy of the data
• CPC Class Codes: G06F 2212/1052 : Security improvement
• CPC Class Codes: G06F 2212/154 : Networked environment
• CPC Class Codes: G06F 2212/163 : Server or database system
• CPC Class Codes: G06F 2212/60 : Details of cache memory
• CPC Class Codes: G06F 3/0481 : based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G06F 3/0482 : Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/0483 : Interaction with page-structured environments, e.g. book metaphor
• CPC Class Codes: G06F 3/04847 : Interaction techniques to control parameter settings, e.g. interaction with sliders or dials
• CPC Class Codes: G06F 3/0485 : Scrolling or panning
• CPC Class Codes: G06F 3/04895 : Guidance during keyboard input operation, e.g. prompting
• CPC Class Codes: G06F 3/0605 : by facilitating the interaction with a user or administrator
• CPC Class Codes: G06F 3/0656 : Data buffering arrangements
• CPC Class Codes: G06F 3/067 : Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]
• CPC Class Codes: G06F 40/117 : Tagging; Marking up details of markup languages G06F40/143; Designating a block; Setting of attributes style sheets, e.g. eXtensible Stylesheet Language Transformation [XSLT], G06F40/154
• CPC Class Codes: G06F 40/134 : Hyperlinking
• CPC Class Codes: G06F 40/166 : Editing, e.g. inserting or deleting
• CPC Class Codes: G06F 40/174 : Form filling; Merging
• CPC Class Codes: G06F 40/177 : of tables; using ruled lines
• CPC Class Codes: G06F 40/18 : of spreadsheets form-filling G06F40/174
• CPC Class Codes: G06F 40/183 : Tabulation, i.e. one-dimensional positioning
• CPC Class Codes: G06F 40/216 : using statistical methods
• CPC Class Codes: G06F 40/274 : Converting codes to words; Guess-ahead of partial word inputs
• CPC Class Codes: G06F 8/30 : Creation or generation of source code
• CPC Class Codes: G06F 8/41 : Compilation
• CPC Class Codes: G06F 8/427 : Parsing
• CPC Class Codes: G06F 8/60 : Software deployment
• CPC Class Codes: G06Q 40/04 : Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: H04L 12/18 : for broadcast or conference , e.g. multicast
• CPC Class Codes: H04L 51/046 : Interoperability with other network applications or services
• CPC Class Codes: H04L 51/212 : using filtering or selective blocking
• CPC Class Codes: H04L 61/5069 : for group communication, multicast communication or broadcast communication
• CPC Class Codes: H04L 63/101 : Access control lists [ACL]
• CPC Class Codes: H04L 63/102 : Entity profiles
• CPC Class Codes: H04L 67/01 : Protocols
• CPC Class Codes: H04L 67/1001 : for accessing one among a plurality of replicated servers
• CPC Class Codes: H04L 67/141 : Setup of application sessions admission control or resource allocation in data switching networks H04L47/70
• CPC Class Codes: H04L 67/34 : involving the movement of software or configuration parameters network booting or remote initial program loading [RIPL] G06F9/4416
• CPC Class Codes: H04L 67/56 : Provisioning of proxy services store-and-forward switching systems in data switching networks H04L12/54
• CPC Class Codes: H04L 67/566 : Grouping or aggregating service requests, e.g. for unified processing
• CPC Class Codes: H04L 67/568 : Storing data temporarily at an intermediate stage, e.g. caching
• CPC Class Codes: H04L 67/5681 : Pre-fetching or pre-delivering data based on network characteristics
• CPC Class Codes: H04L 69/16 : Implementation or adaptation of Internet protocol [IP], of transmission control protocol [TCP] or of user datagram protocol [UDP]

Abstract

Described are methods, systems and computer readable media for data source refreshing.

Claims

1. A system for updating a plurality of data objects using an update propagation graph, the system comprising:

one or more hardware processors coupled to a nontransitory computer readable medium having stored thereon software instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including;

receiving, at a remote query processor, a first query from a client, the first query referencing the plurality of data objects including one or more data sources;

determining dependencies between the plurality of data objects in the first query;

converting first code into in-memory data structures configured to hold a directed acyclic graph (DAG) structure in memory;

updating connectivity within the DAG based on execution of second code;

generating the update propagation graph (UPG) in a context of the remote query processor based on the first query and the DAG, the UPG having a plurality of nodes each corresponding to one of the plurality of data objects, the UPG having a structure representing the dependencies between the plurality of data objects in the first query, wherein a sequence in which the UPG is generated is recorded;

updating the structure of the UPG in response to a second query being executed by the remote query processor, wherein the sequence in which the UPG is updated is recorded; and

after the updating, processing one or more changes according to an order determined by the updated UPG, wherein the sequence determines the order in which the plurality of nodes in the UPG are processed,

wherein the DAG is comprised of one or more of a dynamic node, a static node, or an internal node,

wherein each of the dynamic node, the static node, or the internal node has one or more of input dynamic nodes, input static nodes or input internal nodes as inputs,

wherein the dynamic node includes a node of the DAG that updates as previously presented data corresponding to the dynamic node is received,

wherein the dynamic node includes a timer that triggers an event based on time intervals,

wherein the static node includes a node of the DAG that does not update,

wherein the internal node includes a listener node with associated listener monitoring code that places additional events into a priority queue of the DAG, and

wherein a priority of the event in the priority queue is based on a position of the DAG, and

wherein the internal node includes an interior node of the DAG.

2. The system of claim 1, wherein the data sources include one or more of market data, system generated data, historical data, user input data from a remote user table server, and tables programmatically generated in-memory.

3. The system of claim 1, wherein the data sources include correlation matrices, linear algebra structures, PDE solvers, non-matrix data structures, non-table data structures and mathematical data structures.

4. The system of claim 3, wherein the data sources include a singular value decomposition of another data source.

5. The system of claim 1, wherein the operations further include permitting a program to listen to changes from one or more of the plurality of nodes representing one of one or more of the plurality of data objects or an internal node of the UPG.

6. The system of claim 1, wherein the DAG uses garbage collection to determine whether a part of the DAG should be deleted, wherein upstream links from a node in the DAG have hard links, and downstream links from the node in the DAG have weak links, so that child nodes prevent parent nodes from being garbage collected, and the parent nodes permit the child nodes to be garbage collected.

7. A method for updating one or more data objects using an update propagation graph, the method comprising:

receiving, at a remote query processor, a first query from a client, the first query referencing the one or more data objects including one or more data sources;

determining dependencies between the one or more data objects in the first query;

converting first code into in-memory data structures configured to hold a directed acyclic graph (DAG) structure in memory;

updating connectivity within the DAG based on execution of second code;

generating an update propagation graph (UPG) in a context of the remote query processor based on the first query and the DAG, the UPG having a plurality of nodes each corresponding to one of the one or more data objects, the UPG having a structure representing the dependencies between the one or more data objects in the first query, wherein a sequence in which the UPG is generated is recorded;

updating the structure of the UPG in response to a second query being executed by the remote query processor, wherein the sequence in which the UPG is updated is recorded; and

after the updating, processing one or more changes according to an order determined by the updated UPG, wherein the one or more changes include one or more of addition to, modification of, deletion from or re-indexing of at least part of the one or more data objects,

wherein the DAG is comprised of one or more of a dynamic node, a static node, or an internal node,

wherein each of the dynamic node, the static node, or the internal node has one or more of input dynamic nodes, input static nodes or input internal nodes as inputs,

wherein the dynamic node includes a node of the DAG that updates as previously presented data corresponding to the dynamic node is received,

wherein the dynamic node includes a timer that triggers an event based on time intervals,

wherein the static node includes a node of the DAG that does not update,

wherein the internal node includes a listener node with associated listener monitoring code that places additional events into a priority queue of the DAG, and

wherein a priority of the event in the priority queue is based on a position of the DAG, and

wherein the internal node includes an interior node of the DAG.

8. The method of claim 7, wherein the data sources include one or more of market data, system generated data, historical data, user input data from a remote user table server, and tables programmatically generated in-memory.

9. The method of claim 7, wherein the data sources include correlation matrices, linear algebra structures, PDE solvers, non-matrix data structures, non-table data structures and mathematical data structures.

10. The method of claim 9, wherein the data sources include a singular value decomposition of another data source.

11. The method of claim 7, further comprising permitting a program to listen to changes from one or more of the plurality of nodes representing one of one or more of the one or more data objects or an internal node of the UPG.

12. The method of claim 7, wherein the DAG uses garbage collection to determine whether a part of the DAG should be deleted, wherein upstream links from a node in the DAG have hard links, and downstream links from the node in the DAG have weak links, so that child nodes prevent parent nodes from being garbage collected, and the parent nodes permit the child nodes to be garbage collected.

13. A nontransitory computer readable medium having stored thereon software instructions that, when executed by one or more processors, cause the one or more processors to perform operations including:

receiving, at a remote query processor, a first query from a client, the first query referencing one or more data objects including one or more data sources;

determining dependencies between the one or more data objects in the first query;

converting first code into in-memory data structures configured to hold a directed acyclic graph (DAG) structure in memory;

updating connectivity within the DAG based on execution of second code;

generating an update propagation graph (UPG) in a context of the remote query processor based on the first query and the DAG, the UPG having a plurality of nodes each corresponding to one of the one or more data objects, the UPG having a structure representing the dependencies between the one or more data objects in the first query, wherein a sequence in which the UPG is generated is recorded;

updating the structure of the UPG in response to a second query being executed by the remote query processor, wherein the sequence in which the UPG is updated is recorded; and

after the updating, processing one or more changes according to an order determined by the updated UPG, wherein the sequence determines the order in which the plurality of nodes in the UPG are processed, and wherein the one or more changes include one or more of addition to, modification of, deletion from or re-indexing of at least part of the one or more data objects, and wherein information about the addition, the deletion, and the modification from an upstream node is used to compute changes to downstream nodes,

wherein the DAG is comprised of one or more of a dynamic node, a static node, or an internal node,

wherein each of the dynamic node, the static node, or the internal node has one or more of input dynamic nodes, input static nodes or input internal nodes as inputs,

wherein the dynamic node includes a node of the DAG that updates as previously presented data corresponding to the dynamic node is received,

wherein the dynamic node includes a timer that triggers an event based on time intervals,

wherein the static node includes a node of the DAG that does not update,

wherein the internal node includes a listener node with associated listener monitoring code that places additional events into a priority queue of the DAG, and

wherein a priority of the event in the priority queue is based on a position of the DAG, and

wherein the internal node includes an interior node of the DAG.

14. The nontransitory computer readable medium of claim 13, wherein the data sources include one or more of market data, system generated data, historical data, user input data from a remote user table server, and tables programmatically generated in-memory.

15. The nontransitory computer readable medium of claim 13, wherein the operations further include:

converting first code into in-memory data structures configured to hold a directed acyclic graph (DAG) structure in memory; and

updating connectivity within the DAG based on execution of second code.

16. The nontransitory computer readable medium of claim 15, wherein the DAG is comprised of one or more of a dynamic node, a static node, or an internal node,

wherein the DAG comprises one or more of static subgraphs or dynamic subgraphs,

wherein the dynamic node includes a node of the DAG that updates as previously presented data corresponding to the dynamic node is received,

wherein the dynamic node is a timer that triggers an event based on time intervals,

wherein the static node includes a node of the DAG that does not update, and

wherein the internal node includes an interior node of the DAG.