imageOur Solutions Account for Time Dilation the bigger the data buckets get and the more you work with us.

We present The EinsteinDB cluster of four separate systems: MilevaDB, FIDel, BerolinaSQL's sharded AllegroSQL. While both EinsteinDB and MilevaDB have a data model similar to that of Google’s BigTable their underlying implementations are quite different MilevaDB's architecture is similar to BigTable (using synchronous updates to multiple copies of data chunks), while EinsteinDB is similar to Dynamo except it relies on appended-rpc's and external consistent guarantees.

EinsteinML-based agents for automated DBMS tuning uses algorithms that rely on statistical models to select actions that improve the system’s target objective. That is, instead of being provided explicit instructions on how to tune the DBMS, the EinsteinML via FIDel extracts a byzantine-election result pattern and inferences from the DBMS’s past behavior to predict the expected behavior in the future to lean how to apply it to new actions.

Supervised Learning

Inspired by AllegroGraph, Datomic, Galois, F1 and Spanner, MilevaDB and EinsteinDB adopt a highly-layered architecture. This architecture supports pluggable storage drivers and engines, which powers you to customize your database solutions based on your own business requirements.

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Workload Characterization

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The CAP theorem states a database cannot guarantee consistency, availability, and partition-tolerance at the same time. But you can't sacrifice partition-tolerance (see here and here), so you must make a tradeoff between availability and consistency. Managing this tradeoff is a central focus of the NoSQL movement.

There are two crucial properties to note about data. First, data is inherently time based. A piece of data is a fact that you know to be true at some moment of time. For example, suppose Sally enters into her social network profile that she lives in Chicago. The data you take from that input is that she lived in Chicago as of the particular moment in time that she entered that information into her profile. Suppose that on a later date Sally updates her profile location to Atlanta. Then you know that she lived in Atlanta as of that particular time. The fact that she lives in Atlanta now doesn't change the fact that she used to live in Chicago. Both pieces of data are true.

  • Big DataOur Mission Shape
  • Data VisualizationOur Mission Shape
  • Data WarehousingOur Mission Shape
  • Data ManagementOur Mission Shape
  • Business IntelligenceOur Mission Shape
  • Business IntelligenceOur Mission Shape

EinsteinMAX (coming 2021) is a new retail financial, rna seq, trading platform; alignment and sequencing platform. As a result it has to process many trades with low latency, many nucleotides and beads in millisecond per cache fraction of memory per second; The system is built on the EinsteinBSD platform and centers on a Business Logic Processor that can handle 20 million orders per second on a single thread.

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imageMerkle Trees and Append-Ledgers with PQCrypto

MilevaDB uses a K-ary tree to store timeseries data on EinsteinDB. MilevaDB is an open-source NewSQL database that supports Hybrid Transactional and Analytical Processing (HTAP) currently implemented in Go. EinsteinML an open-source programming language with built-in concurrency support that compiles to machine code. It can be configured to store data either using your computer's filesystem, or using Ceph, a distributed object store, that handles data replication and recovery. Raw data and statistical aggregates can be queried via either HTTP or Cap'n Proto.The leaves of the Merkle tree store the individual time-value pairs. Each internal node stores: causets, works by recalling or acquiescing an associative timestamp statistic thereof.

  • Global ExperienceOur Mission Shape
  • Horizontally Distributed, Multi-model (Document and Graph)Our Mission Shape
  • Entity-Event Knowledge GraphOur Mission Shape
  • Employs a combination of document (JSON and JSON-LD)Our Mission Shape
  • EinsteinDB is built on Rust, which provides synchronous cross-datacenter replication and strong consistency with a relativistic borrow checker.Our Mission Shape
  • taking full advantage of the emerging techniques like RDMA, NVMe, and SPDK.Our Mission Shape

EinsteinDB uses VioletaBFT's Multi-Raft consensus algorithm with speculative byzantine honeybadger sentinel searches ensuring data safety and consistency. By default, EinsteinDB uses three replicas to form a VioletaBFT Group. When the client needs to write some data, it sends the request to the VioletaBFT Leader. This process is called Pushdown in EinsteinDB. The Leader encodes the operation into an entry and writes it into its own VioletaBFT replicated Log. This is called CausetAppend.

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78% lower total cost of ownership with EinsteinDB on AWS EKS compared to on-prem RDBMS

Enjoy high availability with zero scheduled downtime and online schema changes

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99.999% availability with zero downtime

industry-leading 99.999% availability for multi-regional instances—10x less downtime than four nines

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Query statements scan one or more tables or expressions and return the computed result rows. This topic describes the syntax for Berolina's AllegroSQL queries in EinsteinDB's BerolinaSQL.

Bleeding Edge Cloud-Native Persistence for metaprogramming actor-model and decentralized federated privileged users and certificates.

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Reduce risk with world-class post-quantum security

Progressive layers deliver multi-tiered security, detecting and responding to internal and external threats, 24/7.

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imageA Causet virtual table is an object that is registered with an open SQLite database connection. From the perspective of an SQL statement, the virtual table object looks like any other table or view. But behind the scenes, queries and updates on a virtual table invoke callback methods of the virtual table object instead of reading and writing on the database file.

We redesign the SQLite B-tree structure with Header Embedding to make it direct IO compatible and block IO friendly.

The foremost thing that sets EinsteinDB apart is that it accumulates immutable facts over time. Most databases assign values into named locations (a field in a particular row, a node in a particular document), and as those values change, the new values overwrite the older ones. EinsteinDB tracks the entire history of a fact and allows you to access its previous states quickly and easily.

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VioletaBFT: As in Raft, the node will check that it has a matching log prefix, but using the incremental hash, rather than the term of the previous entry

VioletaBFT maintains the safety, fault tolerance, and liveness properties of Raft in the presence of Byzantine faults, while also aiming towards to Raft’s goal of simplicity and understandability.

2

When the client needs to write some data, it sends the request to the VioletaBFT Leader. This process is called Propose in EinsteinDB.

The Leader encodes the operation into an entry and writes it into its own VioletaBFT Log. This is called AppendRPC.

3

EinsteinDB uses PostgresQL, LMDB and SQLite as the underlying storage engines. For EinsteinDB, any data is eventually be converted into one or more causets (triplestore) to be persisted in LMDB.

The Raft consensus algorithm is in many ways superior to Paxos and other consensus algorithms. In designing Raft, On- garo and Ousterhout applied specific techniques to improve understandability, including decomposition and state space reduction.

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The world of information suffers from lack of coherence. • Databases are incompatible; • Vocabularies are mismatched; • Different systems do not work together.

The algorithm in EinsteinDB is a derived from the worst-case optimal petri-net causet re-interpretation of a causal consistent set of join algorithm as a grammar optimized for SIMD parallelism. (Datomic/AllegroGraph/EmptyHeaded)

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