Hardware concerns are valid. But more importantly, SPEC jAppServer is not a database benchmark. It's a Java App Server benchmark with a database component. To be fair, you have to change
only the database, not the app server, hardware, and everything else too. Not to say that the conclusion is wrong, but it's very weird to say anything about database performance in this circumstance.
In our tests, which are database-only and data-warehouse focused, PG seems to be 3x-5x slower than Oracle on the exact same box, as it does not do data compression and is not as CPU-efficient. Which is quite respectable given the price difference, but may be a big enough gap to keep people who have money to spend coming back to commercial databases.
This is a different kind of issue, really, more like the difference between a CPU and a GPU. At the moment, a good GPU has >100x the performance of a good CPU on a certain class of computations. Column stores will clearly never replace row stores for transaction processing for obvious reasons, but (coupled with a few other architectural decisions) they do exhibit >100x the performance of row stores for the kinds of queries seen in data warehouses.
Also, the two technologies are complementary. The goal is not to replace one thing with another, but to provide more kinds of tools and make them work together. Keep a row store for transaction processing, and feed the data into a column store for analysis in near real-time, much like a video game uses a CPU for AI and a GPU for 3D rendering.
Here are a few of the technical reasons one might choose Vertica over Monet; I'll not get into business issues.
Vertica is designed for large amounts of data, and is optimized for disk based systems. Monet does benchmarks against TPC-H Scale Factor 5 (30 million records, an amount which would fit in main memory) running on Postgres; Vertica does TPC-H Scale factor 1000 (6 billion records) against commercial row stores tuned by people who do such work to make a living.
Vertica runs on multi-node clusters, allowing the cluster to grow as the amount of data grows, while Monet doesn't scale to multiple machines.
There are numerous differences in the transaction systems, update architecure, tolerance of hardware failure, and so on, that make Vertica better suited to the enterprise DW market.
The Vertica business model is to sell a database engine (software to store and query data). Clearly use of standard interfaces is important, otherwise nobody would be able to make use of the product (which really ends up being a component of a larger system or strategy) without going to a heap of trouble. So of course Vertica has:
A JDBC driver
An ODBC driver
An interactive SQL client
A growing list of tested integrations with other software
Buzzwords, yes, but they have a little bit of meaning left.
Grid-enabled means that it works on a "shared nothing" environment, that you can use a networked cluster of commodity computers if one isn't enough to hold the data, and so on. This is in contrast to using one big huge box (big computer, big storage array, or whatever). Of course many databases are similarly grid-enabled.
Column-oriented means that data is stored on disk by column, this makes it fast to process a subset of columns that touch lots of rows, as is typical in data warehouse applications. This is a key architectural difference among databases; Oracle, DB2, etc., are "row stores", while Sybase IQ, Vertica, etc. are "column stores".
Note: I work for Vertica Systems
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Hardware concerns are valid. But more importantly, SPEC jAppServer is not a database benchmark. It's a Java App Server benchmark with a database component. To be fair, you have to change only the database, not the app server, hardware, and everything else too. Not to say that the conclusion is wrong, but it's very weird to say anything about database performance in this circumstance.
In our tests, which are database-only and data-warehouse focused, PG seems to be 3x-5x slower than Oracle on the exact same box, as it does not do data compression and is not as CPU-efficient. Which is quite respectable given the price difference, but may be a big enough gap to keep people who have money to spend coming back to commercial databases.
This is a different kind of issue, really, more like the difference between a CPU and a GPU. At the moment, a good GPU has >100x the performance of a good CPU on a certain class of computations. Column stores will clearly never replace row stores for transaction processing for obvious reasons, but (coupled with a few other architectural decisions) they do exhibit >100x the performance of row stores for the kinds of queries seen in data warehouses.
Also, the two technologies are complementary. The goal is not to replace one thing with another, but to provide more kinds of tools and make them work together. Keep a row store for transaction processing, and feed the data into a column store for analysis in near real-time, much like a video game uses a CPU for AI and a GPU for 3D rendering.
Here are a few of the technical reasons one might choose Vertica over Monet; I'll not get into business issues.
Vertica is designed for large amounts of data, and is optimized for disk based systems. Monet does benchmarks against TPC-H Scale Factor 5 (30 million records, an amount which would fit in main memory) running on Postgres; Vertica does TPC-H Scale factor 1000 (6 billion records) against commercial row stores tuned by people who do such work to make a living.
Vertica runs on multi-node clusters, allowing the cluster to grow as the amount of data grows, while Monet doesn't scale to multiple machines.
There are numerous differences in the transaction systems, update architecure, tolerance of hardware failure, and so on, that make Vertica better suited to the enterprise DW market.
Note: I work for Vertica
Note: I work for Vertica
Vertica is not open source. Not sure where the confusion came from.
Note: I work for Vertica.
Buzzwords, yes, but they have a little bit of meaning left. Grid-enabled means that it works on a "shared nothing" environment, that you can use a networked cluster of commodity computers if one isn't enough to hold the data, and so on. This is in contrast to using one big huge box (big computer, big storage array, or whatever). Of course many databases are similarly grid-enabled. Column-oriented means that data is stored on disk by column, this makes it fast to process a subset of columns that touch lots of rows, as is typical in data warehouse applications. This is a key architectural difference among databases; Oracle, DB2, etc., are "row stores", while Sybase IQ, Vertica, etc. are "column stores". Note: I work for Vertica Systems