Emc IP Holding Company LLC

Emc Corporation

Dell Products Inc.

Mikhail Danilov

Konstantin Buinov

Yohannes Altaye

Mikhail Malygin

Alexander Rakulenko

Ivan Tchoub

Andrey Fomin

Gregory Skripko

Andrey Vsevolodovich Kurilov

Maxim S Trusov

Kirill Viktorovich Gusakov

Igor Medvedev

Andrei Paduroiu

Kirill Zakharov

Chen Xiao Wang

Nickolay Alexandrovich Dalmatov

Maksim Vazhenin

Nikita Gutsalov

Alexander Fedorov

Sergey Koyushev

Vladimir Prikhodko

Shashwat Srivastav

Grigorii Skripko

Mark A O'Connell

Lu Lei

Aleksandr Rakulenko

Stephen J Todd

Yu Teng

Mikhail Borisov

Maria Gavrilova

Sergey Karpenkov

Irina Tavantseva

Ao Sun

Sriram Sankaran

Gary Jialei Wu

Jun Luo

Andrew Robertson

Peter Musial

Subba R Gaddamadugu

Julius Jian Zhu

Wesley Wei Sun

Chun Xi Kenny Chen

Vladislav Eremeev

Alexander Elpaev

Mikhail Edkov

Audrey Kurilov

Aleksander Georgievich Rakulenko

Johannes Altaye

Brett A Menzies

Daniel Brook

Pavel Egorov

Annulment of events written into an ordered event stream (OES) storage system is disclosed. Events can be written to one or more segments of an OES. An event can be invalid and overwriting the event can be desirable. A stream-cut event can be employed to facilitate annulment of the event via altering a portion of a segment of an OES. The stream-cut event can be durable. Annulment can avoid rewriting an entire stream and/or complicating a stream by adding mitigating events without overwriting invalid events. In an aspect, annulment can comprise moving a write cursor according to a stream-cut event. The annulment can be lossy or lossless. In an embodiment, annulment can be performed in single tier OES storage system. In another embodiment, multi-tier OES storage systems can facilitate annulment.

Ordered event stream event annulment in an ordered event stream storage system

The described technology is generally directed towards fine-grained data expiration to reduce storage capacity used in a streaming data storage system. Segments of events are scanned, with each event's payload evaluated to determine whether that event is expired or unexpired. For example, for a log event, an expiration period can be determined based on the event creation time and the event logging level, both of which are typically within the log event's payload. Unexpired events are copied to a new destination segment that replaces the original, scanned source segment; the expired events are deleted when the source segment is deleted, reclaiming storage capacity used by the expired events. Also described is maintaining and using segment and chunk metadata to bypass individual event processing when the metadata indicates that the chunk contains only expired events and can be safely deleted.

Log data management in a streaming data storage system

Event prioritization for an ordered event stream (OES) is disclosed. Unlike conventional prioritization techniques, the disclosed subject matter can be performed by an OES data storage system to provide direct, rather than indirect, control of prioritization. In an embodiment, a prioritized hashed key (PHK) can be determined from an event characteristic and an indicated event priority value based on a selectable priority-sensitive hashing function. As such, events with a same key characteristic but different indicated priorities can have different PHKs, events with different key characteristics but the same indicated priority can have different PHKs, and events with the same key characteristic and the same priority can have a same PHK. An event priority can be inherently comprised in the PHK without needing to explicitly store the priority value with a written event in the OES. Moreover, the disclosed prioritization for the OES can be compatible with OES scaling techniques.

Event prioritization for an ordered event stream

Data retention for a tiered ordered event stream (OES) data storage system is disclosed. Data retention can be applied to demoted data, e.g., data demoted from a first tier to a second tier of data storage devices. The data retention disclosed can avoid demoted events, generally aggregated via a chunk object, file object, etc., from being subject to both a stream-cut operation and a retention period. In embodiments, where a stream-cut instance corresponds to events occurring later in time than a retention end time (RET), all demoted events, e.g., as aggregated by a chunk, file, etc., can be affiliated with the RET. However, where the stream-cut instance corresponds to events occurring earlier in time than the RET, then a portion of the events, e.g., those that will be truncated by performing the stream-cut instance, can be demoted and affiliated with the RET.

Demoted data retention via a tiered ordered event stream data storage system

The described technology is generally directed towards managing data retention policy for stream data stored in a streaming storage system. When a request to truncate a data stream from a certain position (e.g., from a request-specified stream cut) is received, an evaluation is made to determine whether the requested position is within a data retention period as specified by data retention policy. If any data prior to the stream cut position (corresponding to a stream cut time) is within the data retention period, the truncation request is blocked. Otherwise truncation from the stream cut point is allowed to proceed/is performed. Also described is handling automated (e.g., sized based) stream truncation requests with respect to data retention.

Retention management for data streams

Scaling an ordered event stream (OES) based on an information-unit (IU) metric is disclosed. The IU metric can correspond to an amount of computing resources that can be consumed to access information embodied in event data of an event of the OES. In this regard, the amount of computing resources to access the data of the stream event itself can be distinct from an amount of computing resources employed to access information embodied in the data. As such, where an external application, e.g., a reader, a writer, etc., can connect to an OES data storage system, enabling the OES to be scaled in response to burdening of computing resources accessing event information, rather than merely event data, can aid in preservation of an ordering of events accessed from the OES.

Information-unit based scaling of an ordered event stream

Storage of data according to mapped reliability groups (MRGs) of mapped redundant arrays of independent nodes (mapped clusters) is disclosed. Real reliability groups (RRGs) of a real cluster can provide compartmentalization that can reduce a probability of a data loss event occurring in a cluster storage system. MRGs for a mapped cluster employing the RRGs of the real cluster can provide additional compartmentalization that can further reduce a probability of a data loss event occurring. A data protection set can be stored according to a first group of MRGs and a redundant representation of the data protection set can be stored according to a second group of MRGs, wherein a network distance between the first and second group of MRGs is greater than a network distance between constituent MRGs of either the first or second group of MRGs.

Mapped redundant array of independent nodes employing mapped reliability groups for data storage

The described technology is generally directed towards fine-grained data retention management in stream-based data storage systems, which facilitates specifying retention policy at the event level. A fixed retention period corresponding to a retention end time is associated with each event, and the system keeps the longest of the retention end times for each segment of a data stream. Stream cuts are generated which divide the data stream into fragments, and are the basis for truncating the data stream. Each stream cut contains a stream cut retention end time obtained from the longest retention end time of the retention end times of the active segments. When an attempt to truncate a data stream from a stream cut is made, the attempt is blocked if the stream cut's retention end time is in the future, otherwise truncation from the stream cut point is allowed to proceed.

Event level retention management for data streams

Retention of events of an ordered event stream according to at least one triggered retention policy is disclosed. Expiration of events stored in a segment of an ordered event stream (OES) can be desirable. New events can be added to a head of an OES segment, and pruning events from a tail of the OES segment can be desirable. Processing applications can predicate event retention, e.g., restricting expiration of an event, on at least one triggered retention policy. In some embodiments, an additional fixed retention policy can be combined with the triggered retention. The disclosed retention can be performed at the event-level or at less granular levels, e.g., segment-level, OES-level, etc., e.g., via batching of events.

Employing triggered retention in an ordered event stream storage system

The technology describes scanning tree data structures (trees) for multiple processes, at least partly in parallel. A service scans a tree from a beginning tree element to an ending tree element on behalf of a process; while scanning, another process can join in the scan at an intermediate tree element location (e.g., a key). For the subsequent process, the service scans the tree based on the intermediate location to the tree end, thereby visiting tree elements in parallel until the tree end, then continuing from the tree beginning element to the intermediate location for the subsequent process. The service basically completes a full carousel-type revolution for each process. One or more other processes can join an ongoing scan at any time, facilitating further parallel tree element visits, while still obtaining a full scan of the entire set of tree elements. The service handles changing tree versions during the scanning.

Parallel handling of a tree data structure for multiple system processes

Described herein, system that facilitates replication of data in a geographically distributed storage environment. According to an embodiment, a system can comprise determining whether a data storage system is in an active data storage mode or a passive data storage mode. In active data storage mode, the data storage system can accept connections from an application, permit the application to store new data objects, replicate the new data objects to other data storage systems, and accept replicated data. In passive data storage mode, the data storage system can block storage of new data objects by the application while accepting replicated data.

Data replication using active and passive data storage modes

The described technology is generally directed towards fine-grained data event expiration in a streaming data storage system. An event to append is given an expiration period, and the expiration time for the events in a data stream or segment of a data stream is the largest expiration time among events in the data stream or segment. Different segments can have different expiration times for their events. In a segment comprising a group of events, a subgroup of expired events prior to a stream cut are deleted by an expiration task. For a subgroup of unexpired events prior to a stream cut, the expiration task retains (does not delete) the subgroup of events. If a scaling operation is performed on a segment, the new successor segment or segments inherit the largest expiration time of the predecessor segment or segments.

Data expiration for stream storages

Data verification technology for ordered event stream (OES) events written into an ordered event stream storage system is disclosed. The verification technology provides perfect reliability. The verification technology further requires low storage overhead in comparison to typical checksums, storing replicated data, etc. Test event data can be generated in a reproducible manner based upon determined OES metadata. OES metadata can be determined from input received via a user interface, via characteristics of an OES storage system, etc., and can be stored for later use in data verification. The test event data can be stored to a portion of an OES storage system under test. The stored test event data can subsequently be verified by using the stored OES metadata to regenerate test event data for comparison to the stored test event data. The test event ordering can be verified via sequence information included in the stored test event data.

Test data verification for an ordered event stream storage system

Commutative coding in a geographically diverse data storage system is disclosed. Commutative coding can achieve a same result as more conventional hierarchical erasure coding of data, but can be more efficient. Commutative coding can employ Galois Field (GF) based bit-matrix operations. The bit-matrix operations can employ a reduced GF order in associated with expanding elements of input matrixes. A reduced GF order can perform matrix operations at a lower complexity, e.g., employing AND operations for a GF(2) in contrast to XOR operations for a GF(2), where w=4, 8, 16, etc. In an aspect, commutative coding can comprise generating a second-tier coding fragment based on applying a second erasure coding scheme, via bit-matrix operations, to a first-tier encoded fragment, wherein the first-tier encoded fragment is based on an input data fragment and a first erasure coding scheme.

Data protection via commutative erasure coding in a geographically diverse data storage system

A geographically diverse data storage system that can protect data via replication of data among relevant zones according to a determined replication topology is disclosed. The replication topology can be determined based on replication times between the relevant zones. In an aspect, a tree topology can provide advantages over a star topography. In an embodiment, a tree topology can be generated, or an existing topology can be modified, via selection of a next replication task(s) based on the replication times. In an aspect, the replication times can be determined from measurable characteristics of the geographically diverse data storage system. In some embodiments, the replications times can be based on historical measurements, time limited historical measurements, inferences from machine learning, etc. A determined topology can be ranked relative to other viable topologies based on criteria such as speed, monetary cost, computing resource usage, etc. Accordingly, a selected topology, or selected modification to a topology, can provide for improved replication that can provide protection for data stored in the geographically diverse data storage system.

Geographically diverse data storage system employing a replication tree

Growing community of inventors

Saint Petersburg, Russia

Mikhail Danilov