Resource Encryption & Compression

Read-only external tables were introduced in Oracle 9i and are now commonplace in most database applications that need to “import” flat-file data. Developers resource Encryption & Compression are unfamiliar with external tables can read about the evolution of this feature in the following oracle-developer. Given the potential for large dump files and the need to transfer this data across networks, Oracle 11g enables us to compress the dataset as it is written to file. To see the effect of compression with writeable external tables, we will need to compare it with an uncompressed external dataset.

First, however, we need an Oracle directory to write the external table data to. We will now create a simple external table by unloading all of the data in the ALL_OBJECTS view, as follows. The default file option for this table is no compression. To compress the dump file, we need to explicitly enable compression using a new 11g access parameter, as follows.

Why do we need header compression? What is the minimum or maximum HPACK state size? How can I avoid keeping HPACK state? Why is there an EOS symbol in HPACK? Is the priority example in Section 5. 1 has served the Web well for more than fifteen years, but its age is starting to show.

HTTP practically only allows one outstanding request per TCP connection. This has led the industry to a place where it’s considered Best Practice to do things like spriting, data: inlining, domain sharding and concatenation. These hacks are indications of underlying problems in the protocol itself, and cause a number of problems on their own when used. 2 was developed by the IETF’s HTTP Working Group, which maintains the HTTP protocol. It’s made up of a number of HTTP implementers, users, network operators and HTTP experts. Note that while our mailing list is hosted on the W3C site, this is not a W3C effort.

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Tim Berners-Lee and the W3C TAG are kept up-to-date with the WG’s progress, however. Firefox, Chrome, Twitter, Microsoft’s HTTP stack, Curl and Akamai, as well as a number of HTTP implementers in languages like Python, Ruby and NodeJS. Github’s contributor graph, and who’s implementing on our implementation list. Since then, there have been a number of changes, based on discussion in the Working Group and feedback from implementers.

2, including both Mike Belshe and Roberto Peon. 2, there’s just one code path. 2 isn’t usable through telnet, but we already have some tool support, such as a Wireshark plugin. This, in turn, allows a client to use just one connection per origin to load a page. Since many sites use multiple origins, this could mean that a single page load opens more than thirty connections. That’s not counting response time – that’s just to get them out of the client.

This overhead is considerable, especially when you consider the impact upon mobile clients, which typically see round-trip latency of several hundred milliseconds, even under good conditions. 2 proposed using a single GZIP context in each direction for header compression, which was simple to implement as well as efficient. As a result, we could not use GZIP compression. HTTP headers often don’t change between messages, this still gives reasonable compression efficiency, and is much safer. That’s because HTTP is so widely used. 2 and back with no loss of information.

Doing that would just create friction against the adoption of the new protocol. As such, we can work on new mechanisms that are version-independent, as long as they’re backwards-compatible with the existing Web. 2 as well, if they’re already using HTTP. APIs don’t need to consider things like request overhead in their design. Having said that, the main focus of the improvements we’re considering is the typical browsing use cases, since this is the core use case for the protocol.

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2 is supported by the most current releases of Edge, Safari, Firefox and Chrome. See the caniuse for more details. Open Source implementations that you can deploy and test. See the implementations list for more details. 2 works well, it should be possible to support new versions of HTTP much more easily than in the past.

Implementation Questions Why the rules around Continuation on HEADERS frames? 16KiB – 1, which means it couldn’t fit into a single frame. RST all streams until a SETTINGS frame with the ACK bit set has been received. The original proposals had stream groups, which would share context, flow control, etc.

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0-7 bits of padding needed for any particular string. HPACK’s design allows for bytewise comparison of huffman-encoded strings. By requiring that the bits of the EOS symbol are used for padding, we ensure that users can do bytewise comparison of huffman-encoded strings to determine equality. This in turn means that many headers can be interpreted without being huffman decoded.

Resource Encryption & Compression

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Stream B has weight 4, stream C has weight 12. Stream B therefore receives one-quarter of the available resources and stream C receives three-quarters. Even for a client-side implementation that only downloads a lot of data using a single stream, some packets will still be necessary to send back in the opposite direction to achieve maximum transfer speeds. This works with both Firefox and Chrome. This can improve the time to retrieve a resource, particularly for connections with a large bandwidth-delay product where the network round trip time comprises most of the time spent on a resource. Pushing resources that vary based on the contents of a request could be unwise. Some caches don’t respect variations in all request header fields, even if they are listed in the Vary header field.

To maximize the likelihood that a pushed resource will be accepted, content negotiation is best avoided. Content negotiation based on the accept-encoding header field is widely respected by caches, but other header fields might not be as well supported. Advanced Index Compression improves the compression ratios significantly while still providing efficient access to the index. Processing of large volumes of data is significantly faster than the exact aggregation, especially for data sets with a large number of distinct values, with negligible deviation from the exact result.

The need to count distinct values is a common operation in today’s data analysis. Optimizing the processing time and resource consumption by orders of magnitude while providing almost exact results speeds up any existing processing and enables new levels of analytical insight. Attribute Clustering Attribute clustering is a table-level directive that clusters data in close physical proximity based on the content of certain columns. This directive applies to any kind of direct path operation, such as a bulk insert or a move operation. Storing data that logically belongs together in close physical proximity can greatly reduce the amount of data to be processed and can lead to better compression ratios.

Automatic Big Table Caching In previous releases, in-memory parallel query did not work well when multiple scans contended for cache memory. This feature implements a new cache called big table cache for table scan workload. This big table cache provides significant performance improvements for full table scans on tables that do not fit entirely into the buffer cache. Full Database Caching Full database caching can be used to cache the entire database in memory. It should be used when the buffer cache size of the database instance is greater than the whole database size. In Oracle RAC systems, for well-partitioned applications, this feature can be used when the combined buffer caches of all instances, with some extra space to handle duplicate cached blocks between instances, is greater than the database size. More specifically, this feature improves the performance of full table scans by forcing all tables to be cached.

This is a change from the default behavior in which larger tables are not kept in the buffer cache for full table scans. CPU and memory efficient KEY VECTOR and VECTOR GROUP BY aggregation operations. These operations may be automatically chosen by the SQL optimizer based on cost estimates. In-Memory Aggregation improves performance of star queries and reduces CPU usage, providing faster and more consistent query performance and supporting a larger number of concurrent users. As compared to alternative SQL execution plans, performance improvements are significant. Greater improvements are seen in queries that include more dimensions and aggregate more rows from the fact table. The columnar format enables scans, joins and aggregates to perform much faster than the traditional on-disk formats for analytical style queries.

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The in-memory columnar format does not replace the on-disk or buffer cache format, but is an additional, transaction-consistent copy of the object. The last few years have witnessed a surge in the concept of in-memory database objects to achieve improved query response times. In-Memory Column Store allows seamless integration of in-memory objects into an existing environment without having to change any application code. By allocating memory to In-Memory Column Store, you can instantly improve the performance of their existing analytic workload and enable interactive ad-hoc data extrapolation.

Resource Encryption & Compression

Oracle Database and allows the database to enforce that JSON stored in the Oracle Database conforms to the JSON rules. This feature also allows JSON data to be queried using a PATH based notation and adds new operators that allow JSON PATH based queries to be integrated into SQL operations. Companies are adopting JSON as a way of storing unstructured and semi-structured data. As the volume of JSON data increases, it becomes necessary to be able to store and query this data in a way that provides similar levels of security, reliability and availability as are provided for relational data.

This feature allows information represented as JSON data to be treated inside the Oracle database. 140 cryptographic processing mode inside the Oracle database. Use of FIPS 140 validated cryptographic modules are increasingly required by government agencies and other industries around the world. Customers who have FIPS 140 requirements can turn on the DBFIPS_140 parameter.

The CONTAINERS clause accepts a table or view name as an input parameter that is expected to exist in all PDBs in that container. This feature enables an innovative way to aggregate user-created data in a multitenant container database. Reports that require aggregation of data across many regions or other attributes can leverage the CONTAINERS clause and get data from one single place. When not set, the PDB inherits the value from the root container. The directory must have appropriate permissions that allow Oracle to create files in it. Oracle generates unique names for the files. A file created in this manner is an Oracle-managed file.

This feature helps administrators to plug or to unplug databases from one container to another in a shared storage environment. If a PDB LOGGING clause is not specified in the CREATE PLUGGABLE DATABASE statement, the logging attribute of the PDB defaults to LOGGING. PDB Metadata Clone An administrator can now create a clone of a pluggable database only with the data model definition. The dictionary data in the source is copied as is but all user-created table and index data from the source is discarded.

This feature enhances cloning functionality and facilitates rapid provisioning of development environments. PDB Remote Clone The new release of Oracle Multitenant fully supports remote full and snapshot clones over a database link. Remote snapshot cloning is also supported across two CDBs sharing the same storage. This feature further improves rapid provisioning of pluggable databases. Administrators can spend less time on provisioning and focus more on other innovative operations. The source of the clone must remain read-only while the target needs to be on a file system that supports sparseness.

Resource Encryption & Compression

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Snapshot cloning support is now extended to other third party vendor systems. This feature eases the requirement of specific file systems for snapshot clones of pluggable databases. With file system agnostic snapshot clones, pluggable databases can be provisioned even faster than before. The STANDBYS clause takes two values: ALL and NONE. If SAVE STATE is specified, open mode of specified PDB is preserved across CDB restart on instances specified in the INSTANCES clause.

Similarly, with the DISCARD STATE clause, the open mode of specified PDB is no longer preserved. These new SQL clauses provide the flexibility to choose the automatic startup of application PDBs when a CDB undergoes a restart. This feature enhances granular control and effectively reduces downtime of an application in planned or unplanned outages. CDB where data belonging to each tenant is kept in a separate PDB. This powerful clause helps convert cumbersome schema-based consolidations to more agile and efficient pluggable databases.

Rapid Home Provisioning Rapid Home Provisioning allows deploying of Oracle homes based on gold images stored in a catalog of pre-created homes. Provisioning time for Oracle Database is significantly improved through centralized management while the updating of homes is simplified to linkage. Oracle snapshot technology is used internally to further improve the sharing of homes across clusters and to reduce storage space. O pruning of data based on the physical location of the data on disk, acting like an anti-index. O necessary to satisfy a query, increasing the performance significantly and reducing the resource consumption.

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Scripting on this page enhances content navigation, but does not change the content in any way. Microsoft Windows versions – to find best choice for file format and archiver utility. Programs were tested using default, out-of-the-box compression settings for the selected format, in all cases the compression level was labelled as “normal”, excluding for IZArc where “Maximal” compression level was the default. After each compression test the output archive was extracted in order to test it was identical to input data.

This benchmark is similar in scope and methods to other available comparatives, i. Unlike ZIP it is based on a proprietary algorithm, so no third part freeware software can create RAR files, but due to its huge popularity almost all archive managers supports RAR extraction. Open Source archive format introduced by 7-Zip, providing higher compression ratio than RAR, and now supported by many archive managers. It contains well known reference files widely used for compression benchmarks, representative of different data structures. This is a classic compression benchmark, meant to synthetically evaluate how fast and efficient can be the compression and extraction of files representing various typical data structures. RAR format provided a better compression ratio tahn ZIP, but worse than other non-ZIP formats, and achieved a good speed result, that was comparable with better ZIP compressors.