nodes in group. ➢ ReplicaIndex: It will represent the position of chunk with respective to ec input buffers order. In other words, EC Chunk position in full stripe, in the order of 1 to (data + parity) Input File 1MB - chunk1 Ozone Client c1:block1 c1:block1 1MB - chunk2 1MB - chunk3 1MB - chunk4 1MB - chunk5 1MB - chunk6 blockGrpID:1 1MB c4:chunk4 1MB c5:chunk5 1MB c6:chunk6 1MB parity1 parity1 (c4, c5, c6) 1MB parity2 (c4, c5, c6) 1MB c1:chunk1 1MB c2:chunk2 1MB c3:chunk 3 1MB parity1 (c1, c2, c3) 1MB parity2 (c1, c2, c3) Stripe-1 Stripe-2 N1 N2 N3 N4 N5 c1:block1 EC Write:

读取⽂件 Chunk1 Chunk2 Chunk3 Chunk4 Chunk5 Chunk6 Chunk7 Chunk8 … data1 data2 data3 parity1 parity2 B-2-d B-2-d B-2-d B-2-p B-2-p Chunk2 1MB Chunk3 1MB Parity1 1MB Parity2 1MB Chunk4 1MB 1MB Chunk5 1MB Chunk6 1MB Parity1 1MB Parity2 1MB 条带1 条带2 0 Chunk1 1MB 10 数据在线修复 DN5 C-2 DN1 C-2 DN2 C-2 DN3 C-2 DN4 C-2 EC Container Group 客户端 读取⽂件 Chunk1 Chunk2 Chunk3 Chunk4 Chunk4 Chunk5 Chunk6 Chunk7 Chunk8 … data1 data2 data3 parity1 parity2 B-2-d B-2-d B-2-d B-2-p B-2-p Chunk1 1MB Chunk2 1MB Chunk3 1MB Parity1 1MB Parity2 1MB Chunk4 1MB Chunk5 1MB

读取⽂件 Chunk1 Chunk2 Chunk3 Chunk4 Chunk5 Chunk6 Chunk7 Chunk8 … data1 data2 data3 parity1 parity2 B-2-d B-2-d B-2-d B-2-p B-2-p Chunk2 1MB Chunk3 1MB Parity1 1MB Parity2 1MB Chunk4 1MB 1MB Chunk5 1MB Chunk6 1MB Parity1 1MB Parity2 1MB 条带1 条带2 0 Chunk1 1MB 数据读取在线恢复 DN5 C-2 DN1 C-2 DN2 C-2 DN3 C-2 DN4 C-2 EC Container Group 客户端 读取⽂件 Chunk1 Chunk2 Chunk3 Chunk4 Chunk4 Chunk5 Chunk6 Chunk7 Chunk8 … data1 data2 data3 parity1 parity2 B-2-d B-2-d B-2-d B-2-p B-2-p Chunk1 1MB Chunk2 1MB Chunk3 1MB Parity1 1MB Parity2 1MB Chunk4 1MB Chunk5 1MB

also choose bge-large or bge-small as the embedding model or modify the context window size or text chunk size depending on your computing resources. Qwen 1.5 model families support a maximum of 32K context model_name = "BAAI/bge-base-en-v1.5" ) # Set the size of the text chunk for retrieval Settings.transformations = [SentenceSplitter(chunk_size=1024)] 1.15.3 现在，我们可以设置语言模型和向量模型。Qwen1.5-Chat 支持包括英语和中文 在内的多种语言对话。您可以使用 lists.append(ls1) ls1 = [ls[i]] lists.append(ls1) return lists class FAISSWrapper(FAISS): chunk_size = 250 chunk_conent = True score_threshold = 0 def similarity_search_with_score_by_vector( self, embedding:

numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. If installed, must be Version 2.6.2 or higher. • bottleneck: iterator [boolean, default False] Return TextFileReader object for iteration or getting chunks with get_chunk(). chunksize [int, default None] Return TextFileReader object for iteration. See iterating and chunking 2058 try: -> 2059 data = self._reader.read(nrows) 2060 except StopIteration: 2061 if self._first_chunk: ~/sandbox/pandas-release/pandas/pandas/_libs/parsers.pyx in pandas._libs.parsers. �→TextReader

:= make([]*resource, 0, len(chunks)) for i, chunk := range chunks { chunk = bytes.TrimSpace(chunk) if len(chunk) == 0 { continue } r, err := ParseChunk(chunk) if err != nil { log.Errorf("Error processing == nil { continue } resources = append(resources, &resource{BackEndResource: r, sha: sha1.Sum(chunk)}) } return resources } • istio/istio/pkg/mcp/creds/pollingWatcher.go (line 189) // getHashSum

Fluentd 参数，可用于调整 Fluentd 日志转发器的性能。通过这些参数，可 以更改以下 Fluentd 行为： 块和块缓冲大小 块清除行为 块转发重试行为 Fluentd 在名为 chunk（块） 的单个 blob 中收集日志数据 。当 Fluentd 创建一个块时，块被视为处于 stage，在这个阶段，数据会被填充到块中。当块已满时，Fluentd 会将块移到 queue，在块被清除或将其 overflowAction 当队列满时块的行为： throw_exception：发 出一个异常并在日志中显 示。 block：停止对数据进行 块除了，直到缓冲区已用 完的问题被解决为止。 drop_oldest_chunk： 删除旧的块以接受新传入 的块。旧块的价值比新块 要小。 block retryMaxInterval exponential_backoff 重试方法 的最大时间（以秒为单位）。 300s 指定执行块清除的方法： lazy、interval 或 immediate。 指定用于块清除的线程数量。 指定当队列满时的块行为：throw_exception、block 或 drop_oldest_chunk。 指定使用 exponential_backoff 块清理方法时的最大间隔时间（以秒为单位）。 指定当块清除失败时重试的类型： exponential_backoff 或 periodic。

Router&Cache RDMA Read Only Read Only R/W Parallel-Raft Protocol&Storage Serverless Data Chunk Data Chunk Data Chunk • 云原生的本质在于为云这种弹性资源下能够为应用提供 稳定的基础架构，所以云原生数据库相对于传统数据库 最大的不同也在这个方面：弹性 • 对于数据存储的高性能、高稳定性、高拓展、资源成本 ess 模式。 • 通过RDMA，绕过CPU，直接和远端内存通信，在计算与 存储分离、计算与内存分离架构上，提升网络利用率和 性能，也能得到传统数据库网络和性能上一样的体验。 • 底层Data Chunk，采用去中心存储，单体失败不影响数 据的完整性，并且自动自愈(Serverless)。 • 通过跨域数据同步能力，实现多地域数据多活。 这个例子，也给数据库云原生化上的技术架构演进提 供了一个范本，并不是原封不动的迁移就变成云原生

stores, e.g. store.df == store[’df’] – new keywords iterator=boolean, and chunksize=number_in_a_chunk are provided to sup- port iteration on select and select_as_multiple (GH3076) 122 Chapter 1. What’s numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. If installed, must be Version 2.1 or higher. • bottleneck: multiple files, appending to create a single dataframe Reading a csv chunk-by-chunk Reading only certain rows of a csv chunk-by-chunk Reading the first few lines of a frame Reading a file that is compressed

stores, e.g. store.df == store[’df’] – new keywords iterator=boolean, and chunksize=number_in_a_chunk are provided to sup- port iteration on select and select_as_multiple (GH3076) 116 Chapter 1. What’s numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunk- ing and caching to achieve large speedups. If installed, must be Version 2.1 or higher. • bottleneck: multiple files, appending to create a single dataframe Reading a csv chunk-by-chunk Reading only certain rows of a csv chunk-by-chunk Reading the first few lines of a frame Reading a file that is compressed