Akiban Server

SQLAlchemy-AkibanSQLAlchemy-Akiban provides a SQLAlchemy dialect for Akiban, as well asa Core/ORM extension library allowing direct control of Akiban nestedSELECT statements and result sets.RequirementsSQLAlchemy-Akiban depends on:Akiban for Python - thisis an extension for the psycopg2 DBAPI, in order to provide nested result support.SQLAlchemy 0.8 - The dialect has been developed against SQLAlchemy 0.8, whichhas one small API change to support nested result sets. Less criticallyit also supports generation of a WHERE clause using an ORM relationshipattribute (see the example in ORM->Explicit Nesting).ConnectingConnect format is similar to that of a regular Postgresql database:from sqlalchemy import create_engineengine = create_engine("akiban+psycopg2://@localhost:15432/")The Engine above will produce connections when the Engine.connectmethod is called.Nested Result SetsThe dialect introduces a new type called NestedResult, the value ofwhich is a new SQLAlchemy ResultProxy representing a nested result:with engine.begin() as conn: result = conn.execute( "SELECT customer.id, " "(SELECT order.id, order.data " "FROM order " "WHERE customer_id=customer.id) AS order " "FROM customer") for row in result: print "customer id:", row['id'] for order_row in row['order']: print "order id:", order_row['id'] print "order data:", order_row['data']DDL IntegrationCurrently, Akiban requires the GROUPING keyword on all foreign keys. The dialectadds this keyword when emitting DDL for foreign keys:from sqlalchemy import MetaData, Table, Column, String, Integer, ForeignKeymetadata = MetaData()customer = Table('customer', metadata, Column('id', Integer, primary_key=True), Column('name', String(20)),)order = Table('order', metadata, Column('id', Integer, primary_key=True), Column('customer_id', Integer, ForeignKey('customer.id')), Column('order_info', String(20)),)metadata.create_all(conn)Will emit DDL like:CREATE TABLE customer ( id INTEGER NOT NULL GENERATED BY DEFAULT AS IDENTITY, name VARCHAR(20) NULL, PRIMARY KEY (id))CREATE TABLE "order" ( id INTEGER NOT NULL GENERATED BY DEFAULT AS IDENTITY, customer_id INTEGER NULL, order_info VARCHAR(20) NULL, PRIMARY KEY (id), GROUPING FOREIGN KEY(customer_id) REFERENCES customer (id))Nested Select ConstructsMoving up a level, the dialect introduces a new Core construct nestedwhich is an extension of SQLAlchemy's "scalar select" construct. This construct isa drop-in replacement for a standard select() subquery, and is a markerintercepted by the Akiban dialect indicating that column and typing information abouta "nested result" should be carried over from statement to result set:from sqlalchemy import selectfrom sqlalchemy_akiban import nestedsub_stmt = nested([order]).where(order.c.customer_id == customer.c.id).label('o')stmt = select([sub_stmt]).where(customer.c.id == 1)result = conn.execute(stmt)The above will produce SQL like the following:SELECT (SELECT "order".id, "order".customer_id,

Be powerful too. So can “aspirational” needs, such as nice clothing; a person aspires to fit into a certain sphere, so they’re willing to buy the clothes regardless of other factors.Don’t just aim for “faster, better, cheaper.” That typically represents just an incremental change, and it will only be a matter of time before another company does it faster, better or cheaper than can do it. Instead, aim for breakthrough changes, Skok said — pointing to Akiban as an example. The Boston database technology company has developed an entirely different way to organize data than previously available, one which allows for much faster accessing of data and no re-writing of applications, Skok said. “Akiban is a discontinuous innovation in the database world,” he said.If you’re going to pick a fight, pick a big fight. It’s often just as much effort to go after a small fight as a big one.Disruptive innovation shouldn’t have disruptive adoption. If it’s too hard to adopt an innovation, it may never happen. In the case of databases, most new technologies require application re-writes to take advantage of the technologies, which makes them difficult to adopt, said Akiban CEO David McFarlane.Pursue something you believe in. The best value propositions are developed by people who are uniquely suited to tackle the problem. Skok said he’s seen unlikely people find great business success through their passion for the work.Original article here.

"order".order_info FROM "order" WHERE "order".customer_id = customer.id) AS oFROM customer WHERE customer.id = %(id_1)sWithin the result set, nested columns will be targetable not just by column name butalso by column object, and any SQLAlchemy-side type converters in place will take effect for thesecolumns:for row in result: print "customer id:", row[customer.c.id] for order_row in row['order']: print "order id:", order_row[order.c.id] print "order data:", order_row[order.c.data]ORM IntegrationSQLAlchemy-Akiban includes ORM extensions, importable from the sqlalchemy_akiban.orm package.Nested Eager LoadingThe orm.nestedload() and orm.nestedload_all() provide relationship eager loadingmaking usage of an embedded nested result. These are used just like SQLAlchemy's ownorm.joinedload() and orm.subqueryload() functions:from sqlalchemy.orm import relationship, Sessionfrom sqlalchemy.ext.declarative import declarative_basefrom sqlalchemy_akiban import ormBase = declarative_base()class Customer(Base): __table__ = customer orders = relationship("Order")class Order(Base): __table__ = ordersess = Session(engine)for customer in sess.query(Customer).options(orm.nestedload(Customer.orders)): print "customer:", customer.name print "orders:", customer.ordersExplicit NestingThe orm.orm_nested() function acts just like the core nested() construct,except that it is ORM-aware and accepts a Query object; it will invokeQuery style loading, nested into the tuples returned by Query:sess = Session()n = orm.orm_nested(sess.query(Order.id, Order).filter(Customer.orders))q = sess.query(Customer, n).filter(Customer.id == 1)for customer, orders in q: print "customer:", customer.name print "orders:", ordersAbove, we're taking advantage of a new convenience feature in SQLAlchemy 0.8, which is thatwe can pass the Customer.orders class-level attribute directly to Query.filter()in order to generate a correlated WHERE clause. Alternatively, we could just spell this out:query.filter(Customer.id==Order.customer_id)

Open J. Mod. Hydrol. 2020, 10, 45–64. [Google Scholar]Radmanesh, F.; Hemat, J.P.; Behnia, A.; Khond, A.; Mohamad, B.A. Calibration and assessment of HEC-1 model in Roodzard watershed. In Proceedings of the 17th International Conference of River Engineering, University of Shahid Chamran, Ahva, Iran, 26–28 February 2006; pp. 85–99. [Google Scholar]Abdelkarim, A. Hydrological and Hydraulic Modeling of Floods using Watershed Modeling System (WMS), 1st ed.; Al-Akiban Publishing House: Riyadh, Saudi Arabia, 2020; 484p. [Google Scholar]Quan, V.D.; Kittiwet, K. An Assessment of Potential Climate Change Impacts on Flood Risk in Central Vietnam. Eur. Sci. J. 2015, 1, 667–681. [Google Scholar]Ministry of Land, Infrastructure, Transport and Tourism (MLIT). Manual for Economic Evaluation of Flood Control Investment (Draft); River Bureau, Ministry of Land, Infrastructure, Transport and Tourism: Tokyo, Japan, 2005; pp. 1–65. Available online: (accessed on 23 June 2022).Hamed, Y. The hydrogeochemical characterization of groundwater in Gafsa-Sidi Boubaker region (Southwestern Tunisia). Arab. J. Geosci. 2013, 6, 697–710. [Google Scholar] [CrossRef] Figure 1. Geographical location of the study area. Figure 1. Geographical location of the study area. Figure 2. Flowchart for HEC-HMS methodology. Figure 2. Flowchart for HEC-HMS methodology. Figure 3. Geometric data of Khazir River. Figure 3. Geometric data of Khazir River. Figure 4. Flowchart for HEC-RAS methodology. Figure 4. Flowchart for HEC-RAS methodology. Figure 5. Curve values (intensity, duration, frequency) for the study station. Figure 5. Curve values (intensity, duration, frequency) for the study station. Figure 6. Curve values (intensity, duration, and frequency) for the study station. Figure 6. Curve values (intensity, duration,

HandlerPartitioning methods Methods for storing different data on different nodesnoneyes depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)yes using Neo4j FabricReplication methods Methods for redundantly storing data on multiple nodesMulti-source replicationyesCausal Clustering using Raft protocol available in in Enterprise Version onlyMapReduce Offers an API for user-defined Map/Reduce methodsnoyes via Faunus, a graph analytics enginenoConsistency concepts Methods to ensure consistency in a distributed systemImmediate Consistency, Eventual consistency (configurable in cluster mode per master or individual client request)Eventual ConsistencyImmediate ConsistencyCausal and Eventual Consistency configurable in Causal Cluster setupImmediate Consistency in stand-alone modeForeign keys Referential integrityyes Constraint checkingyes Relationships in graphsyes Relationships in graphsTransaction concepts Support to ensure data integrity after non-atomic manipulations of dataACIDACIDACIDConcurrency Support for concurrent manipulation of datayesyesyesDurability Support for making data persistentyesyes Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, HazelcastyesUser concepts Access controlDefault Basic authentication through RDF4J client, or via Java when run with cURL, default token-based in the Workbench or via Rest API, optional access through OpenID or Kerberos single sign-on.User authentification and security via Rexster Graph ServerUsers, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)More information provided by the system vendorGraphDB former name: OWLIMJanusGraph successor of TitanNeo4jSpecific characteristicsOntotext GraphDB is a semantic database engine that allows organizations to build...» moreCompetitive advantagesGraphDB allows you to link text and data in big knowledge graphs. It’s easy to experiment...» moreTypical application scenariosMetadata enrichment and management, linked data publishing, semantic inferencing...» moreKey customers​ GraphDB provides a platform for building next-generation AI and Knowledge Graph...» moreMarket metricsGraphDB is the most utilized semantic triplestore for mission-critical enterprise...» moreLicensing and pricing modelsGraphDB Free is a non-commercial version and is free to use. GraphDB Enterprise edition...» moreNewsWhat Are the Best Graph Database Use Cases in 2025?20 March 2025Modeling Agent Memory20 March 2025LLM Knowledge Graph Builder Front-End Architecture18

Interview by Boston Business JournalMichael Skok spoke on the topic of startup value proposition at the Harvard Innovation Lab on October 11, 2012. Kyle Alspach, VC Editor- Boston Business Journal wrote the following the next day:Michael Skok wants tech entrepreneurs in Boston to deeply analyze their value propositions before launching their startups. But not to fear: The venture capitalist also wants to help startups know what he knows on the subject.“When I started as an entrepreneur, I didn’t have anyone telling me the things I was going to screw up,” Skok said during a talk Thursday at the Harvard Innovation Lab in Allston, the first in a series of four “Startup Secrets” classes.It’s the second time Skok has offered the free classes, which he said aim to give an “unfair competitive advantage” to entrepreneurs. Skok is a partner at North Bridge Venture Partners in Waltham, and formerly CEO of software companies including AlphaBox (acquired by IBM).The first class, “Building a Compelling Value Proposition,” also featured insights from several fast-growing tech companies in the Boston area —Akiban, Apperian and uTest. (The slides from the talk can be found here)Some of the insights from the class:Start with a problem, not an idea. Ideas are “free floating” — and most will never turn into a business unless they solve a fundamental problem. Instead, start with a problem that is either unworkable, unavoidable, urgent or underserved — or some combination of the four.Unworkable or unavoidable problems can spell big opportunity. When a problem is unworkable, find the person who could get fired if it isn’t fixed — that person will be highly motivated to buy your solution. Unavoidable problems — such as accounting, regulation or even aging — will similarly give you a built-in set of potential customers.Non-urgent problems won’t be a priority for potential customers. “You have to have something that’s in their top three priorities” to get a potential customer’s attention, Skok said. Most large enterprises also budget a year in advance, so your solution can’t mean a net increase in cost for the company — so it needs to save money for another area in the business.In consumer business, not all needs are blatant or critical. Obvious critical needs can be the basis for huge businesses, of course. But latent needs — in which a consumer doesn’t know they need it until after they’ve had it (think of Facebook) — can