Practical application:
I have several databases that need to be queried from a single search box. Some of them I have direct access to (they're SQL Server / MySQL), others I can only search via an API.
In an ideal world I would inject all of this data into Elasticsearch and use it to determine relevance. Unfortunately I don't have the resources locally to make that run efficiently. Elastic is taking over 400mb of RAM just while idling without adding any actual data or running queries. It looks like most people using Elasticsearch in production are running machines with 32GB - 64GB of RAM. My organization doesn't have access to anything near that powerful available for this project.
So my next idea is to query all the databases and connect to the API's when the user makes a search. Then I need to analyze the results, determine relevance, and return them to the user. I recognize that this is probably a terrible plan in terms of performance. I'm hoping to use memcached to make things more tolerable.
In my research for finding algorithms to determine relevance, I came across tf-idf. I'm looking to apply this to the results I get back from all the databases.
The actual question
My understanding of tf-idf is that after tokenizing every document in the corpus, you perform a term frequency analysis and then multiply it against the inverse document frequency for the words. The inverse document frequency is calculated by dividing the total document count by the the total number of documents with the term.
The problem with this is that if I'm pulling documents from an API, I don't know the true total number of documents in the corpus. I'm only ever pulling a subset, and based on the way those documents are being pulled they're naturally going to all of the terms in them. Can I still apply tf-idf to this by treating the pool of documents returned by these various sources as a single corpus? What's the best way to go about this?
Bonus question
If you have a suggestion for how to accomplish this without hacking together my own search solution or using Elasticsearch I'm all ears...
As you have noticed Elasticsearch is not built to run in memory constrained environments. If you want to use Elasticsearch, but can't set up a dedicated machine, you might consider using a hosted search solution (e.g. AWS Elasticsearch, Elastic Cloud, Algolia, etc.). Those solutions still cost though!
There are two great alternatives that require a bit more work (but not as much as writing your own search solution). Lucene is the actual Search Engine that Elasticsearch is written on top of. It does still load quite a bit of the underlying data structures into memory, so, depending on the size of the underlying data you want to index, it could still run out of memory. But, you should be able to fit quite a bit more data in a single Lucene index than in an entire Elasticsearch instance.
The other alternative, which I know slightly less about, is Sphinx. It is also a Search Engine. And it also allows you to specify how much memory to allocate for it to use. It stores the rest of the data on disk.
We have ~1Tb of user profiles and need to perform two types operations on them:
random reads and writes (~20k profile updates per second)
queries on predefined dimensions (e.g. for reporting)
For example, if we encounter user in a transaction, we want to update his profile with a URL he came from. At the end of the day we want to see all users who visited particular URL. We don't need joins, aggregations, etc., only filtering by one or several fields.
We don't really care about latency, but need high throughput.
Most databases we looked at belong to one of two categories - key-value DBs with fast random access or batch DBs optimized for querying and analytics.
Key-value storages
Aerospike can store terabyte-scale data and is very well-optimized for fast key-based lookup. However, queries on secondary index are deadly slow, which makes it unsuitable for our purposes.
MongoDB is pretty flexible, but requires too much hardware to handle our load. In addition, we encountered particular issues with massive exports from it.
HBase looks attractive since we already have Hadoop cluster. Yet, it's not really clear how to create secondary index for it and what its performance will be.
Cassandra - may be an option, but we don't have experience with it (if you do, please share it)
Couchbase - may be an option, but we don't have experience with it (if you do, please share it)
Analytic storages
Relational DBMS (e.g. Oracle, PostreSQL) provide both - random access and efficient queries, but we have doubts that they can handle terabyte data.
HDFS / Hive / SparkSQL - excellent for batch processing, but doesn't support indexing. The closest thing is partitioning, but it's not applicable given many-to-many relations (e.g. many users visited many URLs). Also, to our knowledge none of HDFS-backed tools except for HBase support updates, so you can only append new data and read latest version, which is not very convenient.
Vertica has very efficient queries, but updates boil down to rewriting the whole file, so are terribly slow.
(Because of limited experience some of information above may be subjective or wrong, please feel free to comment about it)
Do any of the mentioned databases have useful options that we missed?
Is there any other database(s) optimized for your use case? If not, how would you address this task?
My environment: 8GB Ram Notebook with Ubuntu 14.04, Solr 4.3.1, carrot2workbench 3.10.0
My Solr Index: 15980 documents
My Problem: Cluster all documents with the kmeans algorithm
When I drop off the query in the carrot2workbench (query: :), I always get a Java heap size error when using more than ~1000 Results. I started Solr with -Xms256m -Xmx6g but it still occurs.
Is it really a heap size problem or could it be somewhere else?
Your suspicion is correct, it is a heap size problem, or more precisely, a scalability constraint. Straight from the carrot2 FAQs: http://project.carrot2.org/faq.html#scalability
How does Carrot2 clustering scale with respect to the number and length of documents?
The most important characteristic of Carrot2 algorithms to keep in mind is that they perform in-memory clustering. For this reason, as a rule of thumb, Carrot2 should successfully deal with up to a thousand of documents, a few paragraphs each. For algorithms designed to process millions of documents, you may want to check out the Mahout project.
A developer also posted about this here: https://stackoverflow.com/a/28991477
While the developers recommend Mahout, and this is probably the way to go since you would not be bound by the in-memory clustering constraints as in carrot2, there might be other possibilities, though:
If you really like carrot2 but do not necessarily need k-means, you could take a look at the commercial Lingo3G, based on the "Time of clustering 100000 snippets [s] " field and the (***) remark on http://carrotsearch.com/lingo3g-comparison it should be able to tackle more documents. Check also their FAQ entry on "What is the maximum number of documents Lingo3G can cluster?" on http://carrotsearch.com/lingo3g-faq
Try to minimize the size of your labels on which k-means is performing the clustering. Instead of clustering over all the documents content, try to cluster on the abstract/summary or extract important keywords and cluster on them.
That seems as if Carrot uses much to much memory.
K-means doesn't need a whole lot of memory - one integer per document.
So you should be able to run k-means on millions of documents in memory; even with the document vectors in memory.
16k documents is not a lot, so I don't see why you should run into trouble with a good implementation yet. Seems they really want you to buy the commercial version to make a living! Going Mahout seems like overkill to me. Your data still fits into main memory, I guess, so don't waste time on distributing it over the network which is a million times slower than your memory.
Maybe implement k-means yourself. It's not difficult...
Closed. This question is opinion-based. It is not currently accepting answers.
Want to improve this question? Update the question so it can be answered with facts and citations by editing this post.
Closed 4 years ago.
Improve this question
There are several types of database for different purposes, however normally MySQL is used to everything, because is the most well know Database. Just to give an example in my company an application of big data has a MySQL database at an initial stage, what is unbelievable and will bring serious consequences to the company. Why MySQL? Just because no one know how (and when) should use another DBMS.
So, my question is not about vendors, but type of databases. Can you give me an practical example of specific situations (or apps) for each type of database where is highly recommended to use it?
Example:
• A social network should use the type X because of Y.
• MongoDB or couch DB can't support transactions, so Document DB is not good to an app for a bank or auctions site.
And so on...
Relational: MySQL, PostgreSQL, SQLite, Firebird, MariaDB, Oracle DB, SQL server, IBM DB2, IBM Informix, Teradata
Object: ZODB, DB4O, Eloquera, Versant , Objectivity DB, VelocityDB
Graph databases: AllegroGraph, Neo4j, OrientDB, InfiniteGraph, graphbase, sparkledb, flockdb, BrightstarDB
Key value-stores: Amazon DynamoDB, Redis, Riak, Voldemort, FoundationDB, leveldb, BangDB, KAI, hamsterdb, Tarantool, Maxtable, HyperDex, Genomu, Memcachedb
Column family: Big table, Hbase, hyper table, Cassandra, Apache Accumulo
RDF Stores: Apache Jena, Sesame
Multimodel Databases: arangodb, Datomic, Orient DB, FatDB, AlchemyDB
Document: Mongo DB, Couch DB, Rethink DB, Raven DB, terrastore, Jas DB, Raptor DB, djon DB, EJDB, denso DB, Couchbase
XML Databases: BaseX, Sedna, eXist
Hierarchical: InterSystems Caché, GT.M thanks to #Laurent Parenteau
I found two impressive articles about this subject. All credits to highscalability.com. The information in this answer is transcribed from these articles:
35+ Use Cases For Choosing Your Next NoSQL Database
What The Heck Are You Actually Using NoSQL For?
If Your Application Needs...
• complex transactions because you can't afford to lose data or if you would like a simple transaction programming model then look at a Relational or Grid database.
• Example: an inventory system that might want full ACID. I was very unhappy when I bought a product and they said later they were out of stock. I did not want a compensated transaction. I wanted my item!
• to scale then NoSQL or SQL can work. Look for systems that support scale-out, partitioning, live addition and removal of machines, load balancing, automatic sharding and rebalancing, and fault tolerance.
• to always be able to write to a database because you need high availability then look at Bigtable Clones which feature eventual consistency.
• to handle lots of small continuous reads and writes, that may be volatile, then look at Document or Key-value or databases offering fast in-memory access. Also, consider SSD.
• to implement social network operations then you first may want a Graph database or second, a database like Riak that supports relationships. An in-memory relational database with simple SQL joins might suffice for small data sets. Redis' set and list operations could work too.
• to operate over a wide variety of access patterns and data types then look at a Document database, they generally are flexible and perform well.
• powerful offline reporting with large datasets then look at Hadoop first and second, products that support MapReduce. Supporting MapReduce isn't the same as being good at it.
• to span multiple data-centers then look at Bigtable Clones and other products that offer a distributed option that can handle the long latencies and are partition tolerant.
• to build CRUD apps then look at a Document database, they make it easy to access complex data without joins.
• built-in search then look at Riak.
• to operate on data structures like lists, sets, queues, publish-subscribe then look at Redis. Useful for distributed locking, capped logs, and a lot more.
• programmer friendliness in the form of programmer-friendly data types like JSON, HTTP, REST, Javascript then first look at Document databases and then Key-value Databases.
• transactions combined with materialized views for real-time data feeds then look at VoltDB. Great for data-rollups and time windowing.
• enterprise-level support and SLAs then look for a product that makes a point of catering to that market. Membase is an example.
• to log continuous streams of data that may have no consistency guarantees necessary at all then look at Bigtable Clones because they generally work on distributed file systems that can handle a lot of writes.
• to be as simple as possible to operate then look for a hosted or PaaS solution because they will do all the work for you.
• to be sold to enterprise customers then consider a Relational Database because they are used to relational technology.
• to dynamically build relationships between objects that have dynamic properties then consider a Graph Database because often they will not require a schema and models can be built incrementally through programming.
• to support large media then look storage services like S3. NoSQL systems tend not to handle large BLOBS, though MongoDB has a file service.
• to bulk upload lots of data quickly and efficiently then look for a product that supports that scenario. Most will not because they don't support bulk operations.
• an easier upgrade path then use a fluid schema system like a Document Database or a Key-value Database because it supports optional fields, adding fields, and field deletions without the need to build an entire schema migration framework.
• to implement integrity constraints then pick a database that supports SQL DDL, implement them in stored procedures, or implement them in application code.
• a very deep join depth then use a Graph Database because they support blisteringly fast navigation between entities.
• to move behavior close to the data so the data doesn't have to be moved over the network then look at stored procedures of one kind or another. These can be found in Relational, Grid, Document, and even Key-value databases.
• to cache or store BLOB data then look at a Key-value store. Caching can for bits of web pages, or to save complex objects that were expensive to join in a relational database, to reduce latency, and so on.
• a proven track record like not corrupting data and just generally working then pick an established product and when you hit scaling (or other issues) use one of the common workarounds (scale-up, tuning, memcached, sharding, denormalization, etc).
• fluid data types because your data isn't tabular in nature, or requires a flexible number of columns, or has a complex structure, or varies by user (or whatever), then look at Document, Key-value, and Bigtable Clone databases. Each has a lot of flexibility in their data types.
• other business units to run quick relational queries so you don't have to reimplement everything then use a database that supports SQL.
• to operate in the cloud and automatically take full advantage of cloud features then we may not be there yet.
• support for secondary indexes so you can look up data by different keys then look at relational databases and Cassandra's new secondary index support.
• create an ever-growing set of data (really BigData) that rarely gets accessed then look at Bigtable Clone which will spread the data over a distributed file system.
• to integrate with other services then check if the database provides some sort of write-behind syncing feature so you can capture database changes and feed them into other systems to ensure consistency.
• fault tolerance check how durable writes are in the face power failures, partitions, and other failure scenarios.
• to push the technological envelope in a direction nobody seems to be going then build it yourself because that's what it takes to be great sometimes.
• to work on a mobile platform then look at CouchDB/Mobile couchbase.
General Use Cases (NoSQL)
• Bigness. NoSQL is seen as a key part of a new data stack supporting: big data, big numbers of users, big numbers of computers, big supply chains, big science, and so on. When something becomes so massive that it must become massively distributed, NoSQL is there, though not all NoSQL systems are targeting big. Bigness can be across many different dimensions, not just using a lot of disk space.
• Massive write performance. This is probably the canonical usage based on Google's influence. High volume. Facebook needs to store 135 billion messages a month (in 2010). Twitter, for example, has the problem of storing 7 TB/data per day (in 2010) with the prospect of this requirement doubling multiple times per year. This is the data is too big to fit on one node problem. At 80 MB/s it takes a day to store 7TB so writes need to be distributed over a cluster, which implies key-value access, MapReduce, replication, fault tolerance, consistency issues, and all the rest. For faster writes in-memory systems can be used.
• Fast key-value access. This is probably the second most cited virtue of NoSQL in the general mind set. When latency is important it's hard to beat hashing on a key and reading the value directly from memory or in as little as one disk seek. Not every NoSQL product is about fast access, some are more about reliability, for example. but what people have wanted for a long time was a better memcached and many NoSQL systems offer that.
• Flexible schema and flexible datatypes. NoSQL products support a whole range of new data types, and this is a major area of innovation in NoSQL. We have: column-oriented, graph, advanced data structures, document-oriented, and key-value. Complex objects can be easily stored without a lot of mapping. Developers love avoiding complex schemas and ORM frameworks. Lack of structure allows for much more flexibility. We also have program- and programmer-friendly compatible datatypes like JSON.
• Schema migration. Schemalessness makes it easier to deal with schema migrations without so much worrying. Schemas are in a sense dynamic because they are imposed by the application at run-time, so different parts of an application can have a different view of the schema.
• Write availability. Do your writes need to succeed no matter what? Then we can get into partitioning, CAP, eventual consistency and all that jazz.
• Easier maintainability, administration and operations. This is very product specific, but many NoSQL vendors are trying to gain adoption by making it easy for developers to adopt them. They are spending a lot of effort on ease of use, minimal administration, and automated operations. This can lead to lower operations costs as special code doesn't have to be written to scale a system that was never intended to be used that way.
• No single point of failure. Not every product is delivering on this, but we are seeing a definite convergence on relatively easy to configure and manage high availability with automatic load balancing and cluster sizing. A perfect cloud partner.
• Generally available parallel computing. We are seeing MapReduce baked into products, which makes parallel computing something that will be a normal part of development in the future.
• Programmer ease of use. Accessing your data should be easy. While the relational model is intuitive for end users, like accountants, it's not very intuitive for developers. Programmers grok keys, values, JSON, Javascript stored procedures, HTTP, and so on. NoSQL is for programmers. This is a developer-led coup. The response to a database problem can't always be to hire a really knowledgeable DBA, get your schema right, denormalize a little, etc., programmers would prefer a system that they can make work for themselves. It shouldn't be so hard to make a product perform. Money is part of the issue. If it costs a lot to scale a product then won't you go with the cheaper product, that you control, that's easier to use, and that's easier to scale?
• Use the right data model for the right problem. Different data models are used to solve different problems. Much effort has been put into, for example, wedging graph operations into a relational model, but it doesn't work. Isn't it better to solve a graph problem in a graph database? We are now seeing a general strategy of trying to find the best fit between a problem and solution.
• Avoid hitting the wall. Many projects hit some type of wall in their project. They've exhausted all options to make their system scale or perform properly and are wondering what next? It's comforting to select a product and an approach that can jump over the wall by linearly scaling using incrementally added resources. At one time this wasn't possible. It took custom built everything, but that's changed. We are now seeing usable out-of-the-box products that a project can readily adopt.
• Distributed systems support. Not everyone is worried about scale or performance over and above that which can be achieved by non-NoSQL systems. What they need is a distributed system that can span datacenters while handling failure scenarios without a hiccup. NoSQL systems, because they have focussed on scale, tend to exploit partitions, tend not use heavy strict consistency protocols, and so are well positioned to operate in distributed scenarios.
• Tunable CAP tradeoffs. NoSQL systems are generally the only products with a "slider" for choosing where they want to land on the CAP spectrum. Relational databases pick strong consistency which means they can't tolerate a partition failure. In the end, this is a business decision and should be decided on a case by case basis. Does your app even care about consistency? Are a few drops OK? Does your app need strong or weak consistency? Is availability more important or is consistency? Will being down be more costly than being wrong? It's nice to have products that give you a choice.
• More Specific Use Cases
• Managing large streams of non-transactional data: Apache logs, application logs, MySQL logs, clickstreams, etc.
• Syncing online and offline data. This is a niche CouchDB has targeted.
• Fast response times under all loads.
• Avoiding heavy joins for when the query load for complex joins become too large for an RDBMS.
• Soft real-time systems where low latency is critical. Games are one example.
• Applications where a wide variety of different write, read, query, and consistency patterns need to be supported. There are systems optimized for 50% reads 50% writes, 95% writes, or 95% reads. Read-only applications needing extreme speed and resiliency, simple queries, and can tolerate slightly stale data. Applications requiring moderate performance, read/write access, simple queries, completely authoritative data. A read-only application which complex query requirements.
• Load balance to accommodate data and usage concentrations and to help keep microprocessors busy.
• Real-time inserts, updates, and queries.
• Hierarchical data like threaded discussions and parts explosion.
• Dynamic table creation.
• Two-tier applications where low latency data is made available through a fast NoSQL interface, but the data itself can be calculated and updated by high latency Hadoop apps or other low priority apps.
• Sequential data reading. The right underlying data storage model needs to be selected. A B-tree may not be the best model for sequential reads.
• Slicing off part of service that may need better performance/scalability onto its own system. For example, user logins may need to be high performance and this feature could use a dedicated service to meet those goals.
• Caching. A high performance caching tier for websites and other applications. Example is a cache for the Data Aggregation System used by the Large Hadron Collider.
Voting.
• Real-time page view counters.
• User registration, profile, and session data.
• Document, catalog management and content management systems. These are facilitated by the ability to store complex documents has a whole rather than organized as relational tables. Similar logic applies to inventory, shopping carts, and other structured data types.
• Archiving. Storing a large continual stream of data that is still accessible on-line. Document-oriented databases with a flexible schema that can handle schema changes over time.
• Analytics. Use MapReduce, Hive, or Pig to perform analytical queries and scale-out systems that support high write loads.
• Working with heterogeneous types of data, for example, different media types at a generic level.
• Embedded systems. They don’t want the overhead of SQL and servers, so they use something simpler for storage.
• A "market" game, where you own buildings in a town. You want the building list of someone to pop up quickly, so you partition on the owner column of the building table, so that the select is single-partitioned. But when someone buys the building of someone else you update the owner column along with price.
• JPL is using SimpleDB to store rover plan attributes. References are kept to a full plan blob in S3. (source)
• Federal law enforcement agencies tracking Americans in real-time using credit cards, loyalty cards and travel reservations.
• Fraud detection by comparing transactions to known patterns in real-time.
• Helping diagnose the typology of tumors by integrating the history of every patient.
• In-memory database for high update situations, like a website that displays everyone's "last active" time (for chat maybe). If users are performing some activity once every 30 sec, then you will be pretty much be at your limit with about 5000 simultaneous users.
• Handling lower-frequency multi-partition queries using materialized views while continuing to process high-frequency streaming data.
• Priority queues.
• Running calculations on cached data, using a program friendly interface, without having to go through an ORM.
• Uniq a large dataset using simple key-value columns.
• To keep querying fast, values can be rolled-up into different time slices.
• Computing the intersection of two massive sets, where a join would be too slow.
• A timeline ala Twitter.
Redis use cases, VoltDB use cases and more find here.
This question is almost impossible to answer because of the generality. I think you are looking for some sort of easy answer problem = solution. The problem is that each "problem" becomes more and more unique as it becomes a business.
What do you call a social network? Twitter? Facebook? LinkedIn? Stack Overflow? They all use different solutions for different parts, and many solutions can exist that use polyglot approach. Twitter has a graph like concept, but there are only 1 degree connections, followers and following. LinkedIn on the other hand thrives on showing how people are connected beyond first degree. These are two different processing and data needs, but both are "social networks".
If you have a "social network" but don't do any discovery mechanisms, then you can easily use any basic key-value store most likely. If you need high performance, horizontal scale, and will have secondary indexes or full-text search, you could use Couchbase.
If you are doing machine learning on top of the log data you are gathering, you can integrate Hadoop with Hive or Pig, or Spark/Shark. Or you can do a lambda architecture and use many different systems with Storm.
If you are doing discovery via graph like queries that go beyond 2nd degree vertexes and also filter on edge properties you likely will consider graph databases on top of your primary store. However graph databases aren't good choices for session store, or as general purpose stores, so you will need a polyglot solution to be efficient.
What is the data velocity? scale? how do you want to manage it. What are the expertise you have available in the company or startup. There are a number of reasons this is not a simple question and answer.
A short useful read specific to database selection: How to choose a NoSQL Database?. I will highlight keypoints in this answer.
Key-Value vs Document-oriented
Key-value stores
If you have clear data structure defined such that all the data would have exactly one key, go for a key-value store. It’s like you have a big Hashtable, and people mostly use it for Cache stores or clearly key based data. However, things start going a little nasty when you need query the same data on basis of multiple keys!
Some key value stores are: memcached, Redis, Aerospike.
Two important things about designing your data model around key-value store are:
You need to know all use cases in advance and you could not change the query-able fields in your data without a redesign.
Remember, if you are going to maintain multiple keys around same data in a key-value store, updates to multiple tables/buckets/collection/whatever are NOT atomic. You need to deal with this yourself.
Document-oriented
If you are just moving away from RDBMS and want to keep your data in as object way and as close to table-like structure as possible, document-structure is the way to go! Particularly useful when you are creating an app and don’t want to deal with RDBMS table design early-on (in prototyping stage) and your schema could change drastically over time. However note:
Secondary indexes may not perform as well.
Transactions are not available.
Popular document-oriented databases are: MongoDB, Couchbase.
Comparing Key-value NoSQL databases
memcached
In-memory cache
No persistence
TTL supported
client-side clustering only (client stores value at multiple nodes). Horizontally scalable through client.
Not good for large-size values/documents
Redis
In-memory cache
Disk supported – backup and rebuild from disk
TTL supported
Super-fast (see benchmarks)
Data structure support in addition to key-value
Clustering support not mature enough yet. Vertically scalable (see Redis Cluster specification)
Horizontal scaling could be tricky.
Supports Secondary indexes
Aerospike
Both in-memory & on-disk
Extremely fast (could support >1 Million TPS on a single node)
Horizontally scalable. Server side clustering. Sharded & replicated data
Automatic failovers
Supports Secondary indexes
CAS (safe read-modify-write) operations, TTL support
Enterprise class
Comparing document-oriented NoSQL databases
MongoDB
Fast
Mature & stable – feature rich
Supports failovers
Horizontally scalable reads – read from replica/secondary
Writes not scalable horizontally unless you use mongo shards
Supports advanced querying
Supports multiple secondary indexes
Shards architecture becomes tricky, not scalable beyond a point where you need secondary indexes. Elementary shard deployment need 9 nodes at minimum.
Document-level locks are a problem if you have a very high write-rate
Couchbase Server
Fast
Sharded cluster instead of master-slave of mongodb
Hot failover support
Horizontally scalable
Supports secondary indexes through views
Learning curve bigger than MongoDB
Claims to be faster
I was looking for changing the Database Type of Java Edition 4.1.7 of BDB from BTree to Hash. The Core version had DatabaseType.HASH, DatabaseType.RECNO and DatabaseType.Queue- Are these not supported in the Java Edition. If so is there a reason for dropping these?
David Segleau, Director of Product Management for Berkeley DB here. Generally, we recommend that people ask questions on the Berkeley DB forums. You'll find a large community of active Berkeley DB application developers there.
Yes, Berkeley DB (the original product in C) has B-Tree, Hash, Queue and Recno access methods. Berkeley DB Java Edition only supports B-Tree. The main reason for this is that about 99% of our users use B-Tree for storage and Hash is only used by a small subset of applications.
Some useful technical tidbits around this topic:
Hash is particularly useful for people who have a huge data set and a very small amount of available memory cache. In this particular scenario, a B-Tree might require multiple I/Os in order to fetch the internal index pages (that don't fit in cache) and then fetch the record. Hash can typically access the data record with a single I/O.
Hash is usually not helpful if you want to sequentially access of your data or allow duplicates, since there is no implied ordering in a Hash index.
Most applications have sufficient available memory cache to to hold the internal nodes of a B-tree as well as the most frequently accessed data records. In this much more common scenario, B-tree and Hash will have almost identical performance.
Over the last year the Berkeley DB Java Edition team has been working very closely with customers and application developers using very large data sets (in the 250GB - low TB range). In particular, they have been focusing on how to maximize cache efficiency, improve the cache eviction algorithms and minimize the impact of Java garbage collection. We've found that BDB JE 4.1 performs much better, in terms of cache management and efficiency, especially for data sets that exceed the available cache. For more information on this change, see the BDB JE 4.1.7 changelog on the Berkeley DB download page.
For more information on Hash vs B-Tree access methods in Berkeley DB, see chapter 2 of the BDB Reference Manual (Selecting an Access Method).
I was also trying to understand the same thing. I would too appreciate the possibility of using Hash in berkeley db je as I'm working in the (1) scenario, so with a particular ratio between memory size and dataset size.
Are there any options on this? are you planning to put this back in the future? berkeley db je's site on oracle.com says that access time is constant independently from the dataset size. If you use BTrees, this claim is wrong.