Currently, the source code in the JDK is in an ill-defined encoding. There is no official declaration of the encoding used. It is "mostly ASCII", but the relatively few non-ASCII characters used are not well-defined. In many cases, it is latin-1, but I am pretty certain other encodings are used for e.g. Asian translations.
The Primary Vindex for a table is analogous to a database primary key. Every sharded table must have one defined. A Primary Vindex must be unique: given an input value, it must produce a single keyspace ID.
Secondary Vindexes are additional vindexes against other columns of a table offering optimizations for WHERE clauses that do not use the Primary Vindex. Secondary Vindexes return a single or a limited set of keyspace IDs which will allow VTGate to only target shards where the relevant data is present. In the absence of a Secondary Vindex, VTGate would have to send the query to all shards (called a scatter query).
It is important to note that Secondary Vindexes are only used for making routing decisions. The underlying database shards will most likely need traditional indexes on those same columns, to allow efficient retrieval from the table on the underlying MySQL instances.
A Functional Vindex is a vindex where the column value to keyspace ID mapping is pre-established, typically through an algorithmic function. In contrast, a Lookup Vindex is a vindex that provides the ability to create an association between a value and a keyspace ID, and recall it later when needed. Lookup Vindexes are sometimes also informally referred to as cross-shard indexes.
Consistent lookup vindexes use an alternate approach that makes use of careful locking and transaction sequences to guarantee consistency without using 2PC. This gives the best of both worlds, with the benefit of a consistent cross-shard vindex without paying the price of 2PC. To read more about what makes a consistent lookup vindex different from a standard lookup vindex read our consistent lookup vindexes design documentation.
At any given point on the y-axis, SVT-AV1 can maximize encoding speed compared with any other production encoder. For example, the M8 preset is about as efficient as libvp9 preset 0, but M8 is almost 10 times faster.
Two major open source software decoders are compatible with multiple platforms: dav1d was developed by VideoLAN and the open source community and can serve as an app-level decoder, while Google’s libgav1 is integrated into the Android SDK.
[W]e decided to integrate dav1d into the player for both iOS and Android platforms.
GitHub’s scale is truly a unique challenge. When we first deployed Elasticsearch, it took months to index all of the code on GitHub (about 8 million repositories at the time). Today, that number is north of 200 million, and that code isn’t static: it’s constantly changing and that’s quite challenging for search engines to handle. For the beta, you can currently search almost 45 million repositories, representing 115 TB of code and 15.5 billion documents.
目前是 32 台機器，沒有特別提到記憶體大小，也沒有提到 replication 之類的數字：
Code search runs on 64 core, 32 machine clusters.
然後各種 inverted index 與各種資料在壓縮後只有 25TB：
There are some big wins on the size of the index as well. Remember that we started with 115 TB of content that we want to search. Content deduplication and delta indexing brings that down to around 28 TB of unique content. And the index itself clocks in at just 25 TB, which includes not only all the indices (including the ngrams), but also a compressed copy of all unique content. This means our total index size including the content is roughly a quarter the size of the original data!
