builds on top of the flat key-value store. © 2019 Cloudera, Inc. All rights reserved. 18 BUILDING BLOCKS Use proven technologies - don’t reinvent the wheel • RAFT replication – http://raft.github.io implementation of RAFT - Apache Ratis Library. • Storage Containers – Unit of replication (collection of blocks) ● RocksDB - container metadata • Supported by and battle-tested at Facebook. • OM – a namespace {Store Data Blocks In Containers} Container Container Storage Container Manager {Manage Containers, allocate blocks, certificates, datanodes} Container Container DataNodes {Store Data Blocks In Containers}

their respective _OPT variable. # There is no default; the JVM will autoscale based upon machine # memory size. # export HADOOP_HEAPSIZE_MAX= # The minimum amount of heap to use (Java -Xms). If no unit their respective _OPT variable. # There is no default; the JVM will autoscale based upon machine # memory size. # export HADOOP_HEAPSIZE_MIN= HADOOP_NAMENODE_OPTS=-Xmx102400m （2）查看 NameNode 占用内存 HOT hdfs storagepolicies -unsetStoragePolicy -path xxx （5）查看文件块的分布 bin/hdfs fsck xxx -files -blocks -locations （6）查看集群节点 hadoop dfsadmin -report 5.2.2 测试环境准备 1）测试环境描述 服务器规模：5 台 集群配置：副本数为

option io.hdf.dropna_table (GH4625) • pass thru store creation arguments; can be used to support in-memory stores 1.2.7 DataFrame repr Changes The HTML and plain text representations of DataFrame now show swapaxes,transpose,pop * __iter__,keys,__contains__,__len__,__neg__,__invert__ * convert_objects,as_blocks,as_matrix,values * __getstate__,__setstate__ (compat remains in frame/panel) * __getattr__,__setattr__ non-unique indexing in series via .ix/.loc and __getitem__ (GH4246) – Fixed non-unique indexing memory allocation issue with .ix/.loc (GH4280) • DataFrame.from_records did not accept empty recarrays

. . . . . . . . . . . . . . . . . . . 172 3 Frequently Asked Questions (FAQ) 175 3.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 3.2 PeriodIndex values for columns that contain NA values and have dtype object (GH8778). 1.1.3 Performance • Reduce memory usage when skiprows is an integer in read_csv (GH8681) • Performance boost for to_datetime conversions dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was incorrect

. . . . . . . . . . . . . . . . . . . 166 3 Frequently Asked Questions (FAQ) 169 3.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 3.2 PeriodIndex dtype that utilizes memory based on the level size. In prior versions, the memory usage was a constant 8 bytes per element in each level. In addition, in prior versions, the reported memory usage was incorrect incorrect as it didn’t show the usage for the memory occupied by the underling data array. (GH8456) In [26]: dfi = DataFrame(1,index=pd.MultiIndex.from_product([[’a’],range(1000)]),columns=[’A’]) previous

database URI. You only need to create the engine once per database you are connecting to. For an in-memory sqlite database: 12 Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release sqlalchemy import create_engine # Create your connection. In [44]: engine = create_engine(’sqlite:///:memory:’) This engine can then be used to write or read data to/from this database: In [45]: df = pd.DataFrame({’A’: axis frequency (GH5955) • Bug in downcasting inference with empty arrays (GH6733) • Bug in obj.blocks on sparse containers dropping all but the last items of same for dtype (GH6748) • Bug in unpickling

. . . . . . . . . . . . . . . . . 238 i 4 Frequently Asked Questions (FAQ) 241 4.1 DataFrame memory usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 4.2 Byte-Ordering supports arithmetic with np.ndarray (GH10638) • Support pickling of Period objects (GH10439) • .as_blocks will now take a copy optional argument to return a copy of the data, default is to copy (no change presence of the HTTP Content-Encoding header in the response (GH8685) • Enable writing Excel files in memory using StringIO/BytesIO (GH7074) • Enable serialization of lists and dicts to strings in ExcelWriter

Training Efficiency involves benchmarking the model training process in terms of computation cost, memory cost, amount of training data, and the training latency. It addresses questions like: ● How long the model take to train? ● How many devices are needed for the training? ● Can the model fit in memory? ● How much data would the model need to achieve the desired performance on the given task that go beyond just learning hyper-parameters, and instead search for efficient architectures (layers, blocks, end-to-end models) automatically. A simplistic architecture search could involve just learning the

bear, if we ever accidentally cross paths. We build an associative memory when about them over our lifetime. This associative memory helps us visualize the similarities or differences between a pair of model architecture of the downstream task. In essence, the embedding tables provide us a portable memory bank of knowledge about our domain of interest. This knowledge can be freely used by downstream tasks significant portion of the model size on disk and in memory. Although this comes with the cost of the table taking up significant disk space and memory, this issue can be a bottleneck if the model is going

issues covering multiple cases of similar issues across different components in the same Dapr building blocks. None of the issues were of critical or high severity. We found a vulnerability in a 3rd-party dependency not enabled by default. The vulnerability had the potential to crash a Dapr sidecar with an out-of-memory denial of service attack vector. We found the vulnerability a�er performing the threat modelling framework for building cloud-native applications. It consists of a runtime and a set of building blocks that allow users to move infrastructure-related tasks out of their applications into cloud infrastructure