A tombstone is a deleted record in a replica of a distributed data store.The tombstone is necessary, as distributed data stores use eventual consistency, where only a subset of nodes where the data is stored must respond before an operation is considered to be successful.
If information is deleted in an eventually-consistent distributed data store, the "eventual" part of the eventual consistency causes the information to ooze through the node structure, where some nodes may be unavailable at time of deletion. But a feature of eventual consistency causes a problem in case of deletion, as a node that was unavailable at that time will try to "update" the other nodes that no longer have the deleted entry, assuming that they have missed an insert of information. Therefore, instead of deleting the information, the distributed data store creates a (usually temporary) tombstone record, which is not returned in response to requests.
In order not to fill the data store with trash information, there is a policy to remove tombstones completely. For this, the system checks the age of the tombstone and will remove it after a prescribed time has elapsed. In Apache Cassandra, this elapsed time is set with the
GCGraceSeconds parameter and the process is named Compaction. Compaction consumes system resources and also slows down computation capacity.
Because of the delayed removal, the deleted information will appear as empty, after the content of some columns of a number of records has been deleted. After a compaction, the unused columns will be removed from these records.
Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency, also called optimistic replication, is widely deployed in distributed systems, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent does not usually fulfill these stronger constraints.
Multi-master replication is a method of database replication which allows data to be stored by a group of computers, and updated by any member of the group. All members are responsive to client data queries. The multi-master replication system is responsible for propagating the data modifications made by each member to the rest of the group and resolving any conflicts that might arise between concurrent changes made by different members.
Apache CouchDB is an open-source document-oriented NoSQL database, implemented in Erlang.
Apache Cassandra is a free and open-source, distributed, wide column store, NoSQL database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple datacenters, with asynchronous masterless replication allowing low latency operations for all clients. Cassandra offers the distribution design of Amazon DynamoDB with the data model of Google's Bigtable.
A NoSQL database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but the name "NoSQL" was only coined in the early 21st century, triggered by the needs of Web 2.0 companies. NoSQL databases are increasingly used in big data and real-time web applications. NoSQL systems are also sometimes called "Not only SQL" to emphasize that they may support SQL-like query languages or sit alongside SQL databases in polyglot-persistent architectures.
In computing, a graph database (GDB) is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data. A key concept of the system is the graph. The graph relates the data items in the store to a collection of nodes and edges, the edges representing the relationships between the nodes. The relationships allow data in the store to be linked together directly and, in many cases, retrieved with one operation. Graph databases hold the relationships between data as a priority. Querying relationships is fast because they are perpetually stored in the database. Relationships can be intuitively visualized using graph databases, making them useful for heavily inter-connected data.
Riak is a distributed NoSQL key-value data store that offers high availability, fault tolerance, operational simplicity, and scalability. In addition to the open-source version, it comes in a supported enterprise version and a cloud storage version. Riak implements the principles from Amazon's Dynamo paper with heavy influence from the CAP Theorem. Written in Erlang, Riak has fault tolerant data replication and automatic data distribution across the cluster for performance and resilience.
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop. Traditional SQL queries must be implemented in the MapReduce Java API to execute SQL applications and queries over distributed data. Hive provides the necessary SQL abstraction to integrate SQL-like queries (HiveQL) into the underlying Java without the need to implement queries in the low-level Java API. Since most data warehousing applications work with SQL-based querying languages, Hive aids portability of SQL-based applications to Hadoop. While initially developed by Facebook, Apache Hive is used and developed by other companies such as Netflix and the Financial Industry Regulatory Authority (FINRA). Amazon maintains a software fork of Apache Hive included in Amazon Elastic MapReduce on Amazon Web Services.
Hector is a high-level client API for Apache Cassandra. Named after Hector, a warrior of Troy in Greek mythology, it is a substitute for the Cassandra Java Client, or Thrift, that is encapsulated by Hector. It also has Maven repository access.
The standard column family is a NoSQL object that contains columns of related data. It is a tuple (pair) that consists of a key-value pair, where the key is mapped to a value that is a set of columns. In analogy with relational databases, a standard column family is as a "table", each key-value pair being a "row". Each column is a tuple (triplet) consisting of a column name, a value, and a timestamp. In a relational database table, this data would be grouped together within a table with other non-related data.
Amazon DynamoDB is a fully managed proprietary NoSQL database service that supports key-value and document data structures and is offered by Amazon.com as part of the Amazon Web Services portfolio. DynamoDB exposes a similar data model to and derives its name from Dynamo, but has a different underlying implementation. Dynamo had a multi-leader design requiring the client to resolve version conflicts and DynamoDB uses synchronous replication across multiple data centers for high durability and availability. DynamoDB was announced by Amazon CTO Werner Vogels on January 18, 2012, and is presented as an evolution of Amazon SimpleDB solution.
Oracle NoSQL Database (ONDB) is a NoSQL-type distributed key-value database from Oracle Corporation. It provides transactional semantics for data manipulation, horizontal scalability, and simple administration and monitoring.
Druid is a column-oriented, open-source, distributed data store written in Java. Druid is designed to quickly ingest massive quantities of event data, and provide low-latency queries on top of the data. The name Druid comes from the shapeshifting Druid class in many role-playing games, to reflect the fact that the architecture of the system can shift to solve different types of data problems.
Elliptics is a distributed key-value data storage with open source code. By default it is a classic distributed hash table (DHT) with multiple replicas put in different groups. Elliptics was created to meet requirements of multi-datacenter and physically distributed storage locations when storing huge amount of medium and large files.
CrateDB is a distributed SQL database management system that integrates a fully searchable document-oriented data store. It is open-source, written in Java, based on a shared-nothing architecture, and designed for high scalability. CrateDB includes components from Presto, Lucene, Elasticsearch and Netty.
In distributed computing, a conflict-free replicated data type (CRDT) is a data structure which can be replicated across multiple computers in a network, where the replicas can be updated independently and concurrently without coordination between the replicas, and where it is always mathematically possible to resolve inconsistencies that might come up.
Azure Cosmos DB is Microsoft's proprietary globally-distributed, multi-model database service "for managing data at planet-scale" launched in May 2017. It is schema-agnostic, horizontally scalable and generally classified as a NoSQL database.
ClickHouse is an open-source column-oriented DBMS for online analytical processing (OLAP).
Apache Ignite is a distributed database for high-performance computing with in-memory speed.
YugabyteDB is a free and open-source, distributed, relational, NewSQL database management system designed to handle large amounts of data spanning across multiple availability zones and geographic regions while providing single-digit latency, high availability, and no single point of failure.
Thus, the "eventual" in eventual consistency: if a client reads from a replica that did not get the update with a low enough ConsistencyLevel, it will potentially see old data. [...] There's one more piece to the problem: how do we know when it's safe to remove tombstones? [...] [It] defined a constant, GCGraceSeconds, and had each node track tombstone age locally. Once it has aged past the constant, it can be GC'd during compaction (see MemtableSSTable).
To put this in the context of an example, say we have just created 10 rows of data with three columns each. If half the columns are later deleted, and a compaction has not yet occurred, these columns will show up in get_range_slices queries as empty. Using RangeSlicesQuery as described in the previous section, we would have 10 results returned, but only five of them will have values. More importantly, calls to get (via ColumnQuery) by design assume the Column you are retrieving exists in the store. Therefore if you call get on tombstoned data, null is returned (note: this is different than previous versions of Hector where the underlying NotFoundException was propagated up the stack).
|This database-related article is a stub. You can help Wikipedia by expanding it.|