Ef?cient URL Caching for World Wide Web Crawling
Andrei Z.Broder
IBM TJ Watson Research Center
19Skyline Dr
Hawthorne,NY10532 abroder@https://www.doczj.com/doc/0b9724397.html,
Marc Najork
Microsoft Research
1065La Avenida
Mountain View,CA94043
najork@https://www.doczj.com/doc/0b9724397.html,
Janet L.Wiener
Hewlett Packard Labs
1501Page Mill Road
Palo Alto,CA94304
janet.wiener@https://www.doczj.com/doc/0b9724397.html,
ABSTRACT
Crawling the web is deceptively simple:the basic algorithm is(a) Fetch a page(b)Parse it to extract all linked URLs(c)For all the URLs not seen before,repeat(a)–(c).However,the size of the web (estimated at over4billion pages)and its rate of change(estimated at7%per week)move this plan from a trivial programming exercise to a serious algorithmic and system design challenge.Indeed,these two factors alone imply that for a reasonably fresh and complete crawl of the web,step(a)must be executed about a thousand times per second,and thus the membership test(c)must be done well over ten thousand times per second against a set too large to store in main memory.This requires a distributed architecture,which further complicates the membership test.
A crucial way to speed up the test is to cache,that is,to store in main memory a(dynamic)subset of the“seen”URLs.The main goal of this paper is to carefully investigate several URL caching techniques for web crawling.We consider both practical algo-rithms:random replacement,static cache,LRU,and CLOCK,and theoretical limits:clairvoyant caching and in?nite cache.We per-formed about1,800simulations using these algorithms with vari-ous cache sizes,using actual log data extracted from a massive33 day web crawl that issued over one billion HTTP requests.
Our main conclusion is that caching is very effective–in our setup,a cache of roughly50,000entries can achieve a hit rate of almost80%.Interestingly,this cache size falls at a critical point:a substantially smaller cache is much less effective while a substan-tially larger cache brings little additional bene?t.We conjecture that such critical points are inherent to our problem and venture an explanation for this phenomenon.
Categories and Subject Descriptors
H.4.m[Information Systems]:Miscellaneous;D.2[Software]: Software Engineering;D.2.8[Software Engineering]:Metrics;
D.2.11[Software Engineering]:Software Architectures; H.5.4[Information Interfaces and Presentation]:Hypertext/ Hypermedia—Navigation
General Terms
Algorithms,Measurement,Experimentation,Performance
Keywords
Caching,Crawling,Distributed crawlers,URL caching,Web graph models,Web crawlers
Copyright is held by the author/owner(s).
WWW2003,May20–24,2003,Budapest,Hungary.
ACM1-58113-680-3/03/0005.1.INTRODUCTION
A recent Pew Foundation study[31]states that“[s]earch engines have become an indispensable utility for Internet users”and esti-mates that as of mid-2002,slightly over50%of all Americans have used web search to?nd information.Hence,the technology that powers web search is of enormous practical interest.In this pa-per,we concentrate on one aspect of the search technology,namely the process of collecting web pages that eventually constitute the search engine corpus.
Search engines collect pages in many ways,among them direct URL submission,paid inclusion,and URL extraction from non-web sources,but the bulk of the corpus is obtained by recursively exploring the web,a process known as crawling or spidering.The basic algorithm is
(a)Fetch a page
(b)Parse it to extract all linked URLs
(c)For all the URLs not seen before,repeat(a)–(c) Crawling typically starts from a set of seed URLs,made up of URLs obtained by other means as described above and/or made up of URLs collected during previous crawls.Sometimes crawls are started from a single well connected page,or a directory such as https://www.doczj.com/doc/0b9724397.html,,but in this case a relatively large portion of the web (estimated at over20%)is never reached.See[9]for a discussion of the graph structure of the web that leads to this phenomenon.
If we view web pages as nodes in a graph,and hyperlinks as di-rected edges among these nodes,then crawling becomes a process known in mathematical circles as graph traversal.Various strate-gies for graph traversal differ in their choice of which node among the nodes not yet explored to explore next.Two standard strate-gies for graph traversal are Depth First Search(DFS)and Breadth First Search(BFS)–they are easy to implement and taught in many introductory algorithms classes.(See for instance[34]). However,crawling the web is not a trivial programming exercise but a serious algorithmic and system design challenge because of the following two factors.
1.The web is very large.Currently,Google[20]claims to have
indexed over3billion pages.Various studies[3,27,28]have indicated that,historically,the web has doubled every9-12 months.
2.Web pages are changing rapidly.If“change”means“any
change”,then about40%of all web pages change weekly
[12].Even if we consider only pages that change by a third
or more,about7%of all web pages change weekly[17]. These two factors imply that to obtain a reasonably fresh and
complete snapshot of the web,a search engine must crawl at least 100million pages per day.Therefore,step(a)must be executed about1,000times per second,and the membership test in step(c) must be done well over ten thousand times per second,against a set of URLs that is too large to store in main memory.In addition, crawlers typically use a distributed architecture to crawl more pages in parallel,which further complicates the membership test:it is possible that the membership question can only be answered by a peer node,not locally.
A crucial way to speed up the membership test is to cache a(dy-namic)subset of the“seen”URLs in main memory.The main goal of this paper is to investigate in depth several URL caching tech-niques for web crawling.We examined four practical techniques: random replacement,static cache,LRU,and CLOCK,and com-pared them against two theoretical limits:clairvoyant caching and in?nite cache when run against a trace of a web crawl that issued over one billion HTTP requests.We found that simple caching techniques are extremely effective even at relatively small cache sizes such as50,000entries and show how these caches can be im-plemented very ef?ciently.
The paper is organized as follows:Section2discusses the vari-ous crawling solutions proposed in the literature and how caching ?ts in their model.Section3presents an introduction to caching techniques and describes several theoretical and practical algorithms for caching.We implemented these algorithms under the experi-mental setup described in Section4.The results of our simulations are depicted and discussed in Section5,and our recommendations for practical algorithms and data structures for URL caching are presented in Section6.Section7contains our conclusions and di-rections for further research.
2.CRA WLING
Web crawlers are almost as old as the web itself,and numer-ous crawling systems have been described in the literature.In this section,we present a brief survey of these crawlers(in historical order)and then discuss why most of these crawlers could bene?t from URL caching.
The crawler used by the Internet Archive[10]employs multiple crawling processes,each of which performs an exhaustive crawl of 64hosts at a time.The crawling processes save non-local URLs to disk;at the end of a crawl,a batch job adds these URLs to the per-host seed sets of the next crawl.
The original Google crawler,described in[7],implements the different crawler components as different processes.A single URL server process maintains the set of URLs to download;crawling processes fetch pages;indexing processes extract words and links; and URL resolver processes convert relative into absolute URLs, which are then fed to the URL Server.The various processes com-municate via the?le system.
For the experiments described in this paper,we used the Mer-cator web crawler[22,29].Mercator uses a set of independent, communicating web crawler processes.Each crawler process is re-sponsible for a subset of all web servers;the assignment of URLs to crawler processes is based on a hash of the URL’s host component.
A crawler that discovers an URL for which it is not responsible sends this URL via TCP to the crawler that is responsible for it, batching URLs together to minimize TCP overhead.We describe Mercator in more detail in Section4.
Cho and Garcia-Molina’s crawler[13]is similar to Mercator. The system is composed of multiple independent,communicating web crawler processes(called“C-procs”).Cho and Garcia-Molina consider different schemes for partitioning the URL space,includ-ing URL-based(assigning an URL to a C-proc based on a hash of the entire URL),site-based(assigning an URL to a C-proc based on a hash of the URL’s host part),and hierarchical(assigning an URL to a C-proc based on some property of the URL,such as its top-level domain).
The WebFountain crawler[16]is also composed of a set of in-dependent,communicating crawling processes(the“ants”).An ant that discovers an URL for which it is not responsible,sends this URL to a dedicated process(the“controller”),which forwards the URL to the appropriate ant.
UbiCrawler(formerly known as Trovatore)[4,5]is again com-posed of multiple independent,communicating web crawler pro-cesses.It also employs a controller process which oversees the crawling processes,detects process failures,and initiates fail-over to other crawling processes.
Shkapenyuk and Suel’s crawler[35]is similar to Google’s;the different crawler components are implemented as different pro-cesses.A“crawling application”maintains the set of URLs to be downloaded,and schedules the order in which to download them. It sends download requests to a“crawl manager”,which forwards them to a pool of“downloader”processes.The downloader pro-cesses fetch the pages and save them to an NFS-mounted?le sys-tem.The crawling application reads those saved pages,extracts any links contained within them,and adds them to the set of URLs to be downloaded.
Any web crawler must maintain a collection of URLs that are to be downloaded.Moreover,since it would be unacceptable to download the same URL over and over,it must have a way to avoid adding URLs to the collection more than once.Typically,avoid-ance is achieved by maintaining a set of discovered URLs,cover-ing the URLs in the frontier as well as those that have already been downloaded.If this set is too large to?t in memory(which it often is,given that there are billions of valid URLs),it is stored on disk and caching popular URLs in memory is a win:Caching allows the crawler to discard a large fraction of the URLs without having to consult the disk-based set.
Many of the distributed web crawlers described above,namely Mercator[29],WebFountain[16],UbiCrawler[4],and Cho and Molina’s crawler[13],are comprised of cooperating crawling pro-cesses,each of which downloads web pages,extracts their links, and sends these links to the peer crawling process responsible for it.However,there is no need to send a URL to a peer crawling pro-cess more than once.Maintaining a cache of URLs and consulting that cache before sending a URL to a peer crawler goes a long way toward reducing transmissions to peer crawlers,as we show in the remainder of this paper.
3.CACHING
In most computer systems,memory is hierarchical,that is,there exist two or more levels of memory,representing different trade-offs between size and speed.For instance,in a typical worksta-tion there is a very small but very fast on-chip memory,a larger but slower RAM memory,and a very large and much slower disk memory.In a network environment,the hierarchy continues with network accessible storage and so on.Caching is the idea of storing frequently used items from a slower memory in a faster memory.In the right circumstances,caching greatly improves the performance of the overall system and hence it is a fundamental technique in the design of operating systems,discussed at length in any standard textbook[21,37].In the web context,caching is often mentioned in the context of a web proxy caching web pages[26,Chapter11]. In our web crawler context,since the number of visited URLs be-comes too large to store in main memory,we store the collection of visited URLs on disk,and cache a small portion in main memory.
Caching terminology is as follows:the cache is memory used to store equal sized atomic items.A cache has size if it can store at most items.1At each unit of time,the cache receives a request for an item.If the requested item is in the cache,the situation is called a hit and no further action is needed.Otherwise,the situation is called a miss or a fault.If the cache has fewer than items,the missed item is added to the cache.Otherwise,the algorithm must choose either to evict an item from the cache to make room for the missed item,or not to add the missed item.The caching policy or caching algorithm decides which item to evict.The goal of the caching algorithm is to minimize the number of misses. Clearly,the larger the cache,the easier it is to avoid misses. Therefore,the performance of a caching algorithm is characterized by the miss ratio for a given size cache.
In general,caching is successful for two reasons:
Non-uniformity of requests.Some requests are much more popular than others.In our context,for instance,a link to https://www.doczj.com/doc/0b9724397.html, is a much more common occurrence than a link to the authors’home pages.
Temporal correlation or locality of reference.Current re-quests are more likely to duplicate requests made in the re-cent past than requests made long ago.The latter terminol-ogy comes from the computer memory model–data needed now is likely to be close in the address space to data recently needed.In our context,temporal correlation occurs?rst be-cause links tend to be repeated on the same page–we found that on average about30%are duplicates,cf.Section4.2,and second,because pages on a given host tend to be explored se-quentially and they tend to share many links.For example, many pages on a Computer Science department server are likely to share links to other Computer Science departments in the world,notorious papers,etc.
Because of these two factors,a cache that contains popular re-quests and recent requests is likely to perform better than an ar-bitrary cache.Caching algorithms try to capture this intuition in various ways.
We now describe some standard caching algorithms,whose per-formance we evaluate in Section5.
3.1In?nite cache(INFINITE)
This is a theoretical algorithm that assumes that the size of the cache is larger than the number of distinct requests.Clearly,in this case the number of misses is exactly the number of distinct requests,so we might as well consider this cache in?nite.In any case,it is a simple bound on the performance of any algorithm. 3.2Clairvoyant caching(MIN)
More than35years ago,L′a szl′o Belady[2]showed that if the entire sequence of requests is known in advance(in other words, the algorithm is clairvoyant),then the best strategy is to evict the item whose next request is farthest away in time.This theoretical algorithm is denoted MIN because it achieves the minimum number of misses on any sequence and thus it provides a tight bound on performance.Simulating MIN on a trace of the size we consider requires some care–we explain how we did it in Section4.In Section5we compare the performance of the practical algorithms described below both in absolute terms and relative to MIN.
a stream of data[18,1,11].Therefore,an on-line approach might work as well.)Of course,for simulation purposes we can do a?rst pass over our input to determine the most popular URLs,and then preload the cache with these URLs,which is what we did in our experiments.
The second issue above is the very reason we decided to test STATIC:if STATIC performs well,then the conclusion is that there is little locality of reference.If STATIC performs relatively poorly, then we can conclude that our data manifests substantial locality of reference,that is,successive requests are highly correlated.
3.7Theoretical analyses
Given the practical importance of caching it is not surprising that caching algorithms have been the subject of considerable theoreti-cal scrutiny.Two approaches have emerged for evaluating caching algorithms:adversarial and average case analysis.
Adversarial analysis compares the performance of a given on-line algorithm,such as LRU or CLOCK,against the optimal off-line algorithm MIN[6].The ratio between their respective numbers of misses is called the competitive ratio.If this ratio is no larger than for any possible sequence of requests then the algorithm is called-competitive.If there is a sequence on which this ratio is attained,the bound is said to be tight.Unfortunately for the caching problem,the competitive ratio is not very interesting in practice because the worst case sequences are very arti?cial.For example, LRU is-competitive,and this bound is tight.(Recall that is the size of the cache.)For a cache of size,this bound tells us that LRU will never produce more than times the number of misses produced by MIN.However,for most cache sizes,we observed ratios for LRU of less than1.05(see Figure3),and for all cache sizes,we observed ratios less than2.
Average case analysis assumes a certain distribution on the se-quence of requests.(See[23]and references therein,also[24].) The math is not easy,and the analysis usually assumes that the requests are independent,which means no locality of reference. Without locality of reference,a static cache containing the most popular requests performs optimally on average and the issue is how much worse other algorithms do.For instance,Jelenkovi′c[23] shows that asymptotically,LRU performs at most(;is Euler’s constant=0.577....)times worse than STATIC when the distribution of requests follows a Zipf law.However,such results are not very useful in our case:our requests are highly correlated, and in fact,STATIC performs worse than all the other algorithms, as illustrated in Section5.
4.EXPERIMENTAL SETUP
We now describe the experiment we conducted to generate the crawl trace fed into our tests of the various algorithms.We con-ducted a large web crawl using an instrumented version of the Mer-cator web crawler[29].We?rst describe the Mercator crawler ar-chitecture,and then report on our crawl.
4.1Mercator crawler architecture
A Mercator crawling system consists of a number of crawling processes,usually running on separate machines.Each crawling process is responsible for a subset of all web servers,and consists of a number of worker threads(typically500)responsible for down-loading and processing pages from these servers.
Figure1illustrates the?ow of URLs and pages through each worker thread in a system with four crawling processes.
Each worker thread repeatedly performs the following opera-tions:it obtains a URL from the URL Frontier,which is a disk-based data structure maintaining the set of URLs to be downloaded;downloads the corresponding page using HTTP into a buffer(called a RewindInputStream or RIS for short);and,if the page is an HTML page,extracts all links from the page.The stream of extracted links is converted into absolute URLs and run through the URL Filter, which discards some URLs based on syntactic properties.For ex-ample,it discards all URLs belonging to web servers that contacted us and asked not be crawled.
The URL stream then?ows into the Host Splitter,which assigns URLs to crawling processes using a hash of the URL’s host name. Since most links are relative,most of the URLs(81.5%in our ex-periment)will be assigned to the local crawling process;the others are sent in batches via TCP to the appropriate peer crawling pro-cesses.
Both the stream of local URLs and the stream of URLs received from peer crawlers?ow into the Duplicate URL Eliminator(DUE). The DUE discards URLs that have been discovered previously.The new URLs are forwarded to the URL Frontier for future download. In order to eliminate duplicate URLs,the DUE must maintain the set of all URLs discovered so far.Given that today’s web contains several billion valid URLs,the memory requirements to maintain such a set are signi?cant.Mercator can be con?gured to maintain this set as a distributed in-memory hash table(where each crawling process maintains the subset of URLs assigned to it);however,this DUE implementation(which reduces URLs to8-byte checksums, and uses the?rst3bytes of the checksum to index into the hash table)requires about5.2bytes per URL,meaning that it takes over 5GB of RAM per crawling machine to maintain a set of1billion URLs per machine.These memory requirements are too steep in many settings,and in fact,they exceeded the hardware available to us for this experiment.Therefore,we used an alternative DUE im-plementation that buffers incoming URLs in memory,but keeps the bulk of URLs(or rather,their8-byte checksums)in sorted order on disk.Whenever the in-memory buffer?lls up,it is merged into the disk?le(which is a very expensive operation due to disk latency) and newly discovered URLs are passed on to the Frontier.
Both the disk-based DUE and the Host Splitter bene?t from URL caching.Adding a cache to the disk-based DUE makes it possible to discard incoming URLs that hit in the cache(and thus are du-plicates)instead of adding them to the in-memory buffer.As a result,the in-memory buffer?lls more slowly and is merged less frequently into the disk?le,thereby reducing the penalty imposed by disk latency.Adding a cache to the Host Splitter makes it possi-ble to discard incoming duplicate URLs instead of sending them to the peer node,thereby reducing the amount of network traf?c.This reduction is particularly important in a scenario where the individ-ual crawling machines are not connected via a high-speed LAN(as they were in our experiment),but are instead globally distributed. In such a setting,each crawler would be responsible for web servers “close to it”.
Mercator performs an approximation of a breadth-?rst search traversal of the web graph.Each of the(typically500)threads in each process operates in parallel,which introduces a certain amount of non-determinism to the traversal.More importantly, the scheduling of downloads is moderated by Mercator’s politeness policy,which limits the load placed by the crawler on any partic-ular web server.Mercator’s politeness policy guarantees that no server ever receives multiple requests from Mercator in parallel;in addition,it guarantees that the next request to a server will only be issued after a multiple(typically10)of the time it took to answer the previous request has passed.Such a politeness policy is essen-tial to any large-scale web crawler;otherwise the crawler’s operator becomes inundated with complaints.
Figure1:The?ow of URLs and pages through our Mercator setup 4.2Our web crawl
Our crawling hardware consisted of four Compaq XP1000work-
stations,each one equipped with a667MHz Alpha processor,1.5
GB of RAM,144GB of disk2,and a100Mbit/sec Ethernet con-
nection.The machines were located at the Palo Alto Internet Ex-
change,quite close to the Internet’s backbone.
The crawl ran from July12until September3,2002,although
it was actively crawling only for33days:the downtimes were
due to various hardware and network failures.During the crawl,
the four machines performed1.04billion download attempts,784
million of which resulted in successful downloads.429million of
the successfully downloaded documents were HTML pages.These
pages contained about26.83billion links,equivalent to an average
of62.55links per page;however,the median number of links per
page was only23,suggesting that the average is in?ated by some
pages with a very high number of links.Earlier studies reported
only an average of8links[9]or17links per page[33].We offer
three explanations as to why we found more links per page.First, we con?gured Mercator to not limit itself to URLs found in anchor tags,but rather to extract URLs from all tags that may contain them (e.g.image tags).This con?guration increases both the mean and the median number of links per page.Second,we con?gured it to download pages up to16MB in size(a setting that is signi?cantly higher than usual),making it possible to encounter pages with tens 3Given that on average about30%of the links on a page are du-plicates of links on the same page,one might be tempted to elim-inate these duplicates during link extraction.However,doing so is costly and has little bene?t.In order to eliminate all duplicates, we have to either collect and then sort all the links on a page,or build a hash table of all the links on a page and read it out again. Given that some pages contain tens of thousands of links,such a scheme would most likely require some form of dynamic memory management.Alternatively,we could eliminate most duplicates by maintaining a?xed-sized cache of URLs that are popular on that page,but such a cache would just lower the hit-rate of the global cache while in?ating the overall memory requirements.
number to the number of unique?ngerprints,which we determined using an in-memory hash table on a very-large-memory machine. This data reduction step left us with four condensed host splitter logs(one per crawling machine),ranging from51GB to57GB in size and containing between6.4and7.1billion URLs.
In order to explore the effectiveness of caching with respect to inter-process communication in a distributed crawler,we also ex-tracted a sub-trace of the Host Splitter logs that contained only those URLs that were sent to peer crawlers.These logs contained 4.92billion URLs,or about19.5%of all URLs.We condensed the sub-trace logs in the same fashion.We then used the condensed logs for our simulations.
5.SIMULATION RESULTS
We studied the effects of caching with respect to two streams of URLs:
1.A trace of all URLs extracted from the pages assigned to a
particular machine.We refer to this as the full trace.
2.A trace of all URLs extracted from the pages assigned to
a particular machine that were sent to one of the other ma-
chines for processing.We refer to this trace as the cross sub-trace,since it is a subset of the full trace.
The reason for exploring both these choices is that,depending on other architectural decisions,it might make sense to cache only the URLs to be sent to other machines or to use a separate cache just for this purpose.
We fed each trace into implementations of each of the caching algorithms described above,con?gured with a wide range of cache sizes.We performed about1,800such experiments.We?rst de-scribe the algorithm implementations,and then present our simula-tion results.
5.1Algorithm implementations
The implementation of each algorithm is straightforward.We use a hash table to?nd each item in the cache.We also keep a separate data structure of the cache items,so that we can choose one for eviction.For RANDOM,this data structure is simply a list.For CLOCK,it is a list and a clock handle,and the items also contain“mark”bits.For LRU,it is a heap,organized by last access time.STATIC needs no extra data structure,since it never evicts items.MIN is more complicated since for each item in the cache, MIN needs to know when the next request for that item will be.We therefore describe MIN in more detail.
Let be the trace or sequence of requests,that is,is the item requested at time.We create a second sequence containing the time when next appears in.If there is no further request for after time,we set.Formally,
if s.t.
otherwise.
To generate the sequence,we read the trace backwards, that is,from down to0,and use a hash table with key and value.For each item,we probe the hash table.If it is not found,we set and store in the table.If it is found, we retrieve,set,and replace by in the hash table.
Given,implementing MIN is easy:we read and in parallel,and hence for each item requested,we know when it will be requested next.We tag each item in the cache with the time when it will be requested next,and if necessary,evict the item with the highest value for its next request,using a heap to identify it quickly.
5.2Results
We present the results for only one crawling host.The results for the other three hosts are quasi-identical.Figure2shows the miss rate over the entire trace(that is,the percentage of misses out of all requests to the cache)as a function of the size of the cache.We look at cache sizes from to.In Figure3we present the same data relative to the miss-rate of MIN,the optimum off-line algorithm.The same simulations for the cross-trace are depicted in Figures4and5.
For both traces,LRU and CLOCK perform almost identically and only slightly worse than the ideal MIN,except in the criti-cal region discussed below.RANDOM is only slightly inferior to CLOCK and LRU,while STATIC is generally much worse.There-fore,we conclude that there is considerable locality of reference in the trace,as explained in Section3.6.
For very large caches,STATIC appears to do better than MIN. However,this is just an artifact of our accounting scheme:we only charge for misses and STATIC is not charged for the initial loading of the cache.If STATIC were instead charged misses for the initial loading of its cache,then its miss rate would be of course worse than MIN’s.
STATIC does relatively better for the cross trace than for the full trace.We believe this difference is due to the following fact:in the cross trace,we only see references from a page on one host to a page on a different host.Such references usually are short,that is, they do not go deep into a site and often they just refer to the home page.Therefore,the intersection between the most popular URLs and the cross-trace tends to be larger.
The most interesting fact is that as the cache grows,the miss rate for all of the ef?cient algorithms is roughly constant at70%until the region from to,which we call the critical region.Above,the miss rate drops abruptly to20%after which we see only a slight improvement.
We conjecture that this critical region is due to the following phenomenon.Assume that each crawler thread produces URLs as follows:
With probability,it produces URLs from a global set, which is common to all threads.
With probability,it produces URLs from a local set.This set is speci?c to a particular page on a particular host on which the thread is currently working and is further divided into,the union of a small set of very popular URLs on the host(e.g.home page,copyright notice,etc.)and a larger set of URLs already encountered4on the page,and ,a larger set of less popular URLs on the host.
With probability,it produces URLs chosen at random over the entire set of URLs.
We know that is small because STATIC does not perform well. We also know that is about20%for the full trace because the miss rate does not decrease substantially after the critical region. Therefore,is large and the key factor is the interplay between and.We conjecture that for small caches,all or most of plus maybe the most popular fraction of is in the cache.These URLs account for15-30%of the hits.If is substantially larger than,say ten times larger,increasing the cache has little bene?t
Log cache size 0
20
406080100
A b s o l u t e m i s s r a t e (p e r c e n t )
STATIC RANDOM LRU CLOCK MIN
INFINITE
Log cache size
R e l a t i v e m i s s r a t e (M i n =1)
STATIC RANDOM LRU CLOCK MIN
Figure 2:Miss rate as a function of cache size for the full trace
Figure 3:Relative miss rate (MIN =1)as a function of cache size for the full
trace
Log cache size 0
20406080100
A b s o l u t e m i s s r a t e (p e r c e n t )
STATIC RANDOM LRU CLOCK MIN
INFINITE
Log cache size
R e l a t i v e m i s s r a t e (M i n =1)
STATIC RANDOM LRU CLOCK MIN
Figure 4:Miss rate as a function of cache size for the cross sub-trace
Figure 5:Relative miss rate (MIN =1)as a function of cache size for the cross sub-trace
until a substantial portion of ?ts in the cache.Then the increase
will be linear until all of
is contained in the cache.After ?ts entirely in the cache,we have essentially exhausted the bene?ts of caching,and increasing the cache size further is fruitless.
To test this hypothesis further,we performed two additional short crawls:a half-day crawl using 100threads per process and a two-day crawl using 20threads per process.Each crawl collected URL traces from the Host Splitter of 53–75million URLs,which we condensed in the same manner as described earlier.We also iso-lated the ?rst 70million URLs of the full crawl’s trace,which used 500threads.Figures 6and 7compare the effectiveness of applying CLOCK with various cache sizes to these three traces.As expected,the critical region starts further to the left as the number of threads decreases,because with fewer threads is smaller and we start having a substantial portion of in the cache earlier.
This trend is seen even more clearly in Figures 8and 9where we depict the miss rate as a function of the cache size divided by the number of threads.In this case,the curves corresponding to various
number of threads almost coincide.Thus,for practical purposes the controlling variable is the allocated cache size per thread.
Finally,we investigated how the miss rate varies over time.We
used a cache size
,well past the critical region.As can be seen in Figures 10and 11,the differences are small and the miss rate stabilizes about 1-2billion URLs into the full trace.There is a slight increase towards the end in the full trace,probably due to the fact that by then the crawl is deep into various sites and most URLs have not been seen before.(The in?nite cache also suffers an increase in miss rate.)
Log cache size A b s o l u t e m i s s r a t e (p e r c e n t )
CLOCK-20 threads CLOCK-100 threads
CLOCK-500 threads
Log cache size
A b s o l u t e m i s s r a t e (p e r c e n t )
CLOCK-20 threads CLOCK-100 threads CLOCK-500 threads
Figure 6:Miss rate as a function of cache size for the full trace for various numbers of threads (using CLOCK)Figure 7:Miss rate as a function of cache size for the cross sub-trace for various numbers of threads (using
CLOCK)
Cache size / Number of threads A b s o l u t e m i s s r a t e (p e r c e n t
)
CLOCK-20 threads CLOCK-100 threads CLOCK-500 threads Cache size / Number of threads
A b s o l u t e m i s s r a t e (p e r c e n t
)
CLOCK-20 threads
CLOCK-100 threads CLOCK-500 threads
Figure 8:Miss rate as a function of cache size per thread for the full trace Figure 9:Miss rate as a function of cache size per thread for the cross sub-trace
0246Number of cache probes (billions)0
20406080100
A b s o l u t e m i s s r a t e (p e r c e n t )
STATIC RANDOM LRU CLOCK MIN
INFINITE
0.0
0.5 1.0 1.5
Number of cache probes (billions)
020*********A b s o l u t e m i s s r a t e (p e r c e n t )
STATIC RANDOM LRU CLOCK MIN
INFINITE
Figure 10:Miss rate as a function of time for the full trace
(Cache size =
)Figure 11:Miss rate as a function of time for the cross sub-trace
(Cache size =)
6.CACHE IMPLEMENTATIONS
In this section,we discuss the practical implementation of URL caching for web crawling.
To represent the cache,we need a dictionary,that is,a data struc-ture that ef?ciently supports the basic operations insert,delete and search.In addition,for ef?ciency reasons,we would like to support the caching algorithms discussed above at minimal extra memory cost5.In our case,the dictionary keys are URL?ngerprints,typi-cally8-9bytes long.Let be the length of this?ngerprint in bits. We present two solutions that are quite economical for both space and time:Direct circular chaining and Scatter tables with circular lists.The latter is slightly more complicated but needs only
bits per entry to implement the cache and RANDOM together,and bits to implement the cache and CLOCK together.
For both structures,we use Lampson’s abbreviated keys tech-nique(see[25,p.518]).The idea is to have a hash function
and another function such that given and,the key can be uniquely recovered.Since in our case the keys are URL?ngerprints which are already quite well distributed we can proceed as follows:To hash into a table of size we simply take the most signi?cant bits of the?ngerprint as the hash function and take the least signi?cant bits as the function.Thus instead of storing bits per URL we store bits per URL.We will use the“saved”bits for links among the keys that hash to the same location.
Figure12:Hashing with direct chaining
To describe our structures it is useful to recall hashing with direct chaining.(See e.g.[19,section3.3.10]and references therein).In this method,keys are hashed into a table.The keys are not stored in the table but each table entry contains a pointer to a linked list of all keys that hash to the same entry in the table as illustrated in Figure12.
Direct circular chaining(Figure13)is derived from direct chain-ing as follows:We store all the lists in one array of size,so instead of storing pointers we just store indices in the array,requir-ing bits each.This array will be the array of cache entries,and RANDOM and CLOCK will be implemented on this array. Observe that the size of the hash table can be smaller than,say ,although in this case the average length of the chains is. Using Lampson’s scheme,for each entry in the array we will have to store bits to identify the key and bits for the pointer, that is,a total of bits.
7.CONCLUSIONS AND FUTURE DIREC-
TIONS
After running about1,800simulations over a trace containing 26.86billion URLs,our main conclusion is that URL caching is very effective–in our setup,a cache of roughly50,000entries can achieve a hit rate of almost80%.Interestingly,this size is a critical point,that is,a substantially smaller cache is ineffectual while a substantially larger cache brings little additional bene?t. For practical purposes our investigation is complete:In view of our discussion in Section5.2,we recommend a cache size of between 100to500entries per crawling thread.All caching strategies per-form roughly the same;we recommend using either CLOCK or RANDOM,implemented using a scatter table with circular chains. Thus,for500crawling threads,this cache will be about2MB–completely insigni?cant compared to other data structures needed in a crawler.If the intent is only to reduce cross machine traf?c in a distributed crawler,then a slightly smaller cache could be used.In either case,the goal should be to have a miss rate lower than20%. However,there are some open questions,worthy of further re-search.The?rst open problem is to what extent the crawl order strategy(graph traversal method)affects the caching performance. Various strategies have been proposed[14],but there are indica-tions[30]that after a short period from the beginning of the crawl the general strategy does not matter much.Hence,we believe that caching performance will be very similar for any alternative crawl-ing strategy.We can try to implement other strategies ourselves,but ideally we would use independent crawls.Unfortunately,crawling on web scale is not a simple endeavor,and it is unlikely that we can obtain crawl logs from commercial search engines.
In view of the observed fact that the size of the cache needed to achieve top performance depends on the number of threads,the second question is whether having a per-thread cache makes sense. In general,but not always,a global cache performs better than a collection of separate caches,because common items need to be stored only once.However,this assertion needs to be veri?ed in the URL caching context.
The third open question concerns the explanation we propose in Section5regarding the scope of the links encountered on a given host.If our model is correct then it has certain implications regard-ing the appropriate model for the web graph,a topic of consider-able interest among a wide variety of scientists:mathematicians, physicists,and computer scientists.We hope that our paper will stimulate research to estimate the cache performance under various models.Models where caching performs well due to correlation of links on a given host are probably closer to reality.We are making our URL traces available for this research by donating them to the Internet Archive.
8.ACKNOWLEDGMENTS
We would like to thank Dennis Fetterly for his help in managing our crawling machines and in?ghting the various problems that we encountered during the data collection phase and Torsten Suel for his many valuable comments and suggestions.
9.REFERENCES
[1]N.Alon,Y.Matias,and M.Szegedy.The space complexity
of approximating the frequency moments.Journal of
Computer and System Sciences,58(1):137–147,1999. [2]L.A.Belady.A study of replacement algorithms for a
virtual-storage computer.IBM Systems Journal,5(2):78–101, 1966.
[3]K.Bharat and A.Z.Broder.A technique for measuring the
relative size and overlap of public web search engines.In
Proceedings of the7th World Wide Web Conference,pages
379–388,1998.https://www.doczj.com/doc/0b9724397.html,.au/
programme/fullpapers/1937/com1937.htm. [4]P.Boldi,B.Codenotti,M.Santini,and S.Vigna.Trovatore:
Towards a highly scalable distributed web crawler.In Poster Proceedings of the10th International World Wide Web
Conference,2001.
https://www.doczj.com/doc/0b9724397.html,/cdrom/posters/1033.pdf.
[5]P.Boldi,B.Codenotti,M.Santini,and S.Vigna.Ubicrawler:
A scalable fully distributed web crawler.In Proceedings of
the8th Australian World Wide Web Conference,July2002.
https://www.doczj.com/doc/0b9724397.html,.au/aw02/papers/
refereed/vigna/paper.html.
[6]A.Borodin and R.El-Yaniv.Online computation and
competitive analysis.Cambridge University Press,1998. [7]S.Brin and L.Page.The anatomy of a large-scale
hypertextual Web search engine.In Proceedings of the7th
World Wide Web Conference,pages107–117,1998.
https://www.doczj.com/doc/0b9724397.html,.au/programme/
fullpapers/1921/com1921.htm.
[8]A.Z.Broder.Some applications of Rabin’s?ngerprinting
method.In R.Capocelli,A.De Santis,and U.Vaccaro,
editors,Sequences II:Methods in Communications,Security, and Computer Science,pages143–152.Springer-Verlag,
1993.
[9]A.Z.Broder,R.Kumar,F.Maghoul,P.Raghavan,
S.Rajagopalan,R.Stata,A.Tomkins,and J.L.Wiener.
Graph structure in the Web.In Proceedings of the9th World Wide Web Conference,pages309–320,2000.
https://www.doczj.com/doc/0b9724397.html,/w9cdrom/160/160.html. [10]M.Burner.Crawling towards eternity:Building an archive of
the world wide web.Web Techniques Magazine,2(5),May
1997.
[11]M.Charikar,K.Chen,and M.Farach-Colton.Finding
frequent items in data streams.Lecture Notes in Computer
Science,2380:693–703,2002.
[12]J.Cho and H.Garcia-Molina.The evolution of the web and
implications for an incremental crawler.In Proceedings of
the26th International Conference on Very Large Databases, 2000.https://www.doczj.com/doc/0b9724397.html,/?cho/papers/ cho-evol.pdf.
[13]J.Cho and H.Garcia-Molina.Parallel crawlers.In
Proceedings of the11th International World Wide Web
Conference,2002.
[14]J.Cho,H.Garcia-Molina,and L.Page.Ef?cient crawling
through URL ordering.In Proceedings of the7th
International World Wide Web Conference,pages161–172,
Brisbane,1998.https://www.doczj.com/doc/0b9724397.html,.au/
programme/fullpapers/1919/com1919.htm. [15]F.J.Corbato.A Paging Experiment with the Multics System.
Project MAC Memo MAC-M-384,Massachusetts Institute
of Technology,1968.
[16]J.Edwards,K.S.McCurley,and J.A.Tomlin.An adaptive
model for optimizing performance of an incremental web
crawler.In Proceedings of the10th International World Wide Web Conference,pages106–113,May2001.
[17]D.Fetterly,M.Manasse,M.Najork,and J.L.Wiener.A
large-scale study of the evolution of web pages.In
Proceedings of the12th International World Wide Web
Conference,2003.
[18]P.B.Gibbons and Y.Matias.Synopsis data structures for
massive data sets.In Proceedings of the10th Annual
ACM-SIAM Symposium on Discrete Algorithms(SODA),
pages909–910.ACM Press,1999.
[19]G.H.Gonnet and R.Baeza-Yates.Handbook of algorithms
and data structures:in Pascal and C.Addison-Wesley,
second edition,1991.
[20]Google.https://www.doczj.com/doc/0b9724397.html,.
[21]J.L.Hennessy and https://www.doczj.com/doc/0b9724397.html,puter Architecture a
Quantitive Approach.Morgan Kaufmann,San Francisco,
third edition,2002.
[22]A.Heydon and M.Najork.Mercator:A scalable,extensible
web crawler.World Wide Web,2(4):219–229,1999.
[23]P.R.Jelenkovi′c.Asymptotic approximation of the
move-to-front search cost distribution and least-recently used caching fault probabilities.Ann.Appl.Prob.,9(2):430–464,
1999.Available from http://comet.ctr.columbia.
edu/?predrag/mypub/mtfRevised.ps.
[24]D.E.Knuth.An analysis of optimum caching.Journal of
Algorithms,6(2):181–199,1985.Reprinted as Chapter17of Selected Papers on Analysis of Algorithms by Donald E.
Knuth,Stanford,California,Center for the Study of
Language and Information,2000.
[25]D.E.Knuth.The Art of Computer Programming:Sorting
and Searching.Addison-Wesley,1973.
[26]B.Krishnamurthy and J.Rexford.Web Protocols and
Practice.HTTP/1.1:Networking Protocols,Caching,and
Traf?c Measurement.Addison-Wesley,May2001.
[27]https://www.doczj.com/doc/0b9724397.html,wrence and C.L.Giles.Searching the World Wide
Web.Science,280(5360):98–100,1998.[28]https://www.doczj.com/doc/0b9724397.html,wrence and C.L.Giles.Accessibility of information on
the web.Nature,400:107–109,1999.
[29]M.Najork and A.Heydon.High-performance web crawling.
SRC Research Report173,Compaq Systems Research
Center,Palo Alto,CA,Sept.2001.
[30]M.Najork and J.L.Wiener.Breadth-First Crawling yields
high-quality pages.In Proceedings of the10th International World Wide Web Conference,pages114–118,May2001. [31]Pew Internet and American Life Project Survey.Search
engines:A Pew Internet project data memo,July2002.
https://www.doczj.com/doc/0b9724397.html,/reports/toc.
asp?Report=64.
[32]M.O.Rabin.Fingerprinting by random polynomials.
Technical Report TR-15-81,Center for Research in
Computing Technology,Harvard University,1981.
[33]K.Randall,R.Stata,R.Wickremesinghe,and J.L.Wiener.
The Link Database:Fast Access to Graphs of the Web.In
Proceedings of the Data Compression Conference,pages
122–131,April,2002.
[34]R.Sedgewick.Algorithms in C,Part5:Graph Algorithms.
Addison-Wesley,third edition,2001.
[35]V.Shkapenyuk and T.Suel.Design and implementation of a
high-performance distributed web crawler.In IEEE
International Conference on Data Engineering(ICDE),Feb.
2002.
[36]T.Suel.Personal communication.Jan.2003.
[37]A.S.Tanenbaum and A.S.Woodhull.Operating Systems–
Design and Implementation.Prentice-Hall,second edition,
1997.
初二新学期计划800字； 新的学期已经开始了。我们告别了初一生活，迎来了一个比较重要的时期初二年级。在这个学年中，知识会更加有难度，并且也是个两极分化的时候。在这个学期中，我们可能要遇到各种各样的问题，也会经历一些坎坷，有时候在学习中，更会出现一些困难。所以在这个学期开始之前，我们要为自己制定一份新学期计划，来让这个学年度过的更加有调理，更加顺利。 首先，在学习方面要有好的学习方法，努力做到事半功倍，遇到问题一定不能视而不见，要积极去解决这些问题，平时在课前要做好预习的功课，课堂上也要认真听讲，认真思考，全身心的投入，课后要巩固今天所学到的知识。作业也一定要认真完成好，尽量少出现错误。在学习之余，也可以做一些练习题，让学到的知识变得更加牢固，因为熟能生巧，所以我们一定要注意平时的学习。 其次，是常规和纪律方面。好的习惯是生活中很必要的，所以我们在平时一定要注意常规方面的问题，从点滴做起，任何时候都要严格要求自己，并且时时刻刻遵从老师的要求。纪律是学习的前提，所以在每时每刻，都要遵守校规班规，不违纪。并且我相信自己一定会继续发扬自己的优点，也会努力改正自己在各方面的问题，在严格要求自己的情况下，也要提醒和帮助其他同学，一起让班级整体变得更加完美。 最后，要尽快适应初二的学习生活，做最好的自己。当然，我身
为班干部，一定要处处为班级着想，多为班级建设作出贡献，也要为班级的一些事情出谋划策，尽自己的一份力。不能因为自己而给班级拖后腿，在班级中要积极担当责任，也要有责任心，把老师交给自己的任务落实好。身为团员，在平时，也要多听取同学和老师对自己提出的建议和意见，不断的提高和完善自己。 同时，希望在新的一个学期中，八3班在八年级中，学习成绩排名前列，在全校纪律方面、常规方面做一个佼佼者，在活动方面也可以获得更多的荣誉。做一个成绩优异，踏实而富有特色的班级。同学之间关系融洽，使班级整体更加有凝聚力。希望八3班在每一项活动和评比中，都会做的更加出色! 谢谢阅读！
初二新学期计划600字 新学期打算作文马上就要开学了，开学后我就是初二的学生了。初二的到来，自己慢慢地有一点危机感了，害怕同学超越自己。为了能在初二学的好一点，我决定现在就想好新学期的打算。 第一，我决定学会放弃。在前两个学期里，我经常去上网、看小说、睡觉，对于老师布置的作业也不认真完成。对于这些流失的时间，我不能挽回，我一 定要把握。但是我不怕，放弃了会让我更认真点。那些东西以后来做也一样， 何必就要现在呢？我对自己说。 第二，我决定抓不足。在我的成绩中是不平均的，分数高的和分数低的相 差几十分。以前的两个学期我的思想还不够成熟，想法还比较简单。现在我不 以前那想法简单的自己了，我有了自己的目标，自己的想法，我的记忆也更好了。为了自己的成绩能在平均中上等，我决定在自己擅长的科目不放过，对于 自己不懂的向别人学习。 第三，我决定在每一次周测后都会认真去反思我之前的错误。每一个人都 会有不同的错误，而且也不会没有错误。在以前，每一次周测下来我都会有许 多错误。那些错误的地方看起来我我都知道，但还是做错了。过去我不认真去 反思，去发现自己的错误，去改正它。但是在这个学期，我会去认真改正，我 会以最还得状态去面对新的学期。 还有，在初一我发现有许多人比我努力。在我们班上有一个人，她在初一 的时候英语很不好，比我还差。但是后来她努力了，现在已经在中上了。她现
在还是默默无闻地认真学习，她不懂感认真像我们请教。老师您多次叫我向她学习，这回我认识到了，你是对的，我早都有向她学习了。我有许多的坏习惯了，骄傲是我最大的弱点，我会改的——在这个学期。英语我一定会跟上去，相信我。 新的学期，我会有新的改变。老师请你相信，我会给您带来惊喜！
初二新学期计划范文精选5篇 初二新学期计划1 马上就要开学了，开学后我就是初二的学生了。初二的到来，自己慢慢地有一点危机感了，害怕同学超越自己。为了能在初二学的好一点，我决定现在就想好新学期的打算。 第一，我决定学会放弃。在前两个学期里，我经常去上网、看小说、睡觉，对于老师布置的作业也不认真完成。对于这些流失的时间，我不能挽回，我一定要把握。但是我不怕，放弃了会让我更认真点。那些东西以后来做也一样，何必就要现在呢?我对自己说。 第二，我决定抓不足。在我的成绩中是不平均的，分数高的和分数低的相差几十分。以前的两个学期我的思想还不够成熟，想法还比较简单。现在我不以前那想法简单的自己了，我有了自己的目标，自己的想法，我的记忆也更好了。为了自己的成绩能在平均中上等，我决定在自己擅长的科目不放过，对于自己不懂的向别人学习。 第三，我决定在每一次周测后都会认真去反思我之前的错误。每一个人都会有不同的错误，而且也不会没有错误。在以前，每一次周测下来我都会有许多错误。那些错误的地方看起来我我都知道，但还是做错了。过去我不认真去反思，去发现自己的错误，去改正它。但是在这个学期，我会去认真改正，我会以最还得状
态去面对新的学期。 还有，在初一我发现有许多人比我努力。在我们班上有一个人，她在初一的时候英语很不好，比我还差。但是后来她努力了，现在已经在中上了。她现在还是默默无闻地认真学习，她不懂感认真像我们请教。老师您多次叫我向她学习，这回我认识到了，你是对的，我早都有向她学习了。我有许多的坏习惯了，骄傲是我最大的弱点，我会改的——在这个学期。英语我一定会跟上去，相信我。 新的学期，我会有新的改变。老师请你相信，我会给您带来惊喜! 初二新学期计划2 凡事预则立，不预则废。新的学期我确立了新的目标，“学习成绩进位争先，能力素质全面发展”，为此，我制定了新学期计划。 在文化课学习上，坚持课前预习、课后复习，按时完成老师布置的作业，上课认真听讲，积极思考，踊跃发言，平时遇到学习上的问题及时请教，多与同学交流，不让问题过夜。在语文学习方面，侧重多读，关心国家大事，养成每天读报的习惯，坚持每周安排半天课外阅读，一个月看一本中外名著，一学期看一集中学生范文选，扩大知识面，提高作文水平;在数学学习方面，侧重多练，每天晚上坚持一小时数学练习，每周坚持半天奥数学习，及时巩固和深化课堂学习，拓宽解题思路，提高解题速度;
绘制世界中国地图的般步骤 一、世界地图 1. 欧洲、地中海部分麻烦，大家对该轮廓关注程度高，先画 2. 接下来画非洲、阿拉伯半岛，控制好框架 3.
绘制世界中国地图的般步骤42北冰洋沿岸可以简略处理，但要控制好白令海峡处的位置 3.
6.3.再画南亚、印度洋海岸 7. 84从堪察加半岛一笔画至越南南部，停顿观察与南亚部分的衔接，看看是否需要 修 改。
10.5.澳大利亚、南北美洲相对容易一些，但控制好框架最重要 12.6.画上赤道是检验世界地图画得成功与否的关键， 画得不好 露位置不准。 9. 11. 条直线过来就会暴
7. 有了轮廓图，就可以讲述海洋中的洋流、陆地上的山脉、河流、城市 8. 当然画一下完整的大西洋海岸是很有必要的。 二、中国地图 1. 从我国最北端“ 一笔三弯”绘到我国最东点 主要是针对版面，可控制中国地图的高度和最东，同时该线也决定了中国地图的大 小。当然，不管先画那一处，第一笔都决定着整个中国地图的大小。 2. 由北向下绘出中国雄鸡的头和嘴 该笔最受关注，整个中国图好不好，就看雄鸡的头和嘴。 3. 由西北端向东绘“三弯一提”。 4. 基本可以控制东西方的地图宽度，最东点、鸡嘴、西北阿尔泰几乎在一水平线, 13.
最东点一鸡嘴与鸡嘴一西北阿尔泰长度大约1:2 o 4. 沿乌苏里江向南“两弯一直” 5. 这一笔画难度较大，最后完工时往往会使人觉得雄鸡画胖了或者雄鸡头大了??… 这一笔还影响到渤海湾与山东半岛的形态，觉得这一笔决定着中国图的变形与否。 5. 由西北“三弯一平”至最西 6. 心有难处他乡走，常常犯难时就画这笔，缓解压力。该区域国界线关注不高，大致轮廓出来即可，同时也控制了整幅中国图的东西长度，最西端与辽东半岛高度差别不大。 6. 中国海岸线由北至南一笔完成 7. 注意：辽东湾、渤海湾、黄河三角洲、山东半岛、海州湾一线应经常练习，除辽东半岛、渤海、山东半岛外，多大致按半圆弧来绘制。该线条最快、最顺手。 7. 由帕米尔高原到滇藏分界处“两弯一圆弧” 8. 边界处重要城市不多，关注不高，可以简略处理，但要使整个中国地图有形，喀喇昆仑山附近边界和克什米尔地区附近的凹处将是关键之笔。 8. 从云南西北方收尾到广西沿海。 注意：云南滇西和滇南有两处凸出，滇南凸出处与北部湾大致持平，云桂交界处向北近北回归线
2020初中生新学期的打算作文800字 新学期新计划 迷迷糊糊，忙忙碌碌，一个寒假又让它溜走了。迎来的，又是一个崭新的开始;崭新的学期，又和一位位同学们站在了同一起跑线上，准备在这一学期里，再次和自己认定的对手比个高低。 新的学期，新的开始，新的打算。在新的学年里，我们要会追求，追求成绩，追求学问，追求我们想得到的，追求是一种伟力。追求，使猿不安于现状最终走出了茹毛饮血的世界;追求使人历尽千难万险 而无悔。左丘明失明著《左传》，司马迁遭宫刑写下史家绝唱。追求，使人为它粉身碎骨而无怨。商鞅将临分身之刑而谈笑自若，谭 嗣同面对屠刀高呼“快哉，快哉!”虽前面有千沟万壑仍奋然东流; 檐头雨珠依恋大地，明知会跌个粉碎，依旧投向大地;大雁心怀故园，千山万水也阻挡不了它归来……追求是一种伟力，永远存在于世间。 既是追求，又何必管它艰难险阻?只要一心一意的追求自己的目标，世界便永远常新。鲁迅先生赞叹第一个吃蟹的人是勇士，为人 类发现了一种佳肴，这也是一种追求;达尔文周游世界，创立进化论 学说，这也是一种追求。追求有大有小，但只要追求新生事物，追 求真理，那么无论匹夫走卒，王侯公卿，他们都是平等的追求者。 我们应该对于他们的追求永报以鼓励的笑颜。他们的精神也将超越 身份、地位、时间、空间而流传下去。 追求的机会均等，可有人善于追求，有人不善于追求，有人追求一生享乐，追求的肤浅;有人为学“孙康映雪”，白天游乐而夜晚读书，追求的虚伪;有人做事一曝十寒，追求无力。追求不是贪欲，不 容人玷污;追求不是勋章，不为标榜;追求不是弹簧，可以随意屈伸，任人扭曲。既要追求，便要追求深远、博大，野心、贪心、私心永 远与追求无缘。
初二新学期计划字 HEN system office room 【HEN16H-HENS2AHENS8Q8-HENH1688】
初二新学期计划600字 新学期打算作文马上就要开学了，开学后我就是初二的学生了。初二的到来，自己慢慢地有一点危机感了，害怕同学超越自己。为了能在初二学的好一点，我决定现在就想好新学期的打算。第一，我决定学会放弃。在前两个学期里，我经常去上网、看小说、睡觉，对于老师布置的作业也不认真完成。对于这些流失的时间，我不能挽回，我一定要把握。但是我不怕，放弃了会让我更认真点。那些东西以后来做也一样，何必就要现在呢我对自己说。 第二，我决定抓不足。在我的成绩中是不平均的，分数高的和分数低的相差几十分。以前的两个学期我的思想还不够成熟，想法还比较简单。现在我不以前那想法简单的自己了，我有了自己的目标，自己的想法，我的记忆也更好了。为了自己的成绩能在平均中上等，我决定在自己擅长的科目不放过，对于自己不懂的向别人学习。 第三，我决定在每一次周测后都会认真去反思我之前的错误。每一个人都会有不同的错误，而且也不会没有错误。在以前，每一次周测下来我都会有许多错误。那些错误的地方看起来我我都知道，但还是做错了。过去我不认真去反思，去发现自己的错误，去改正它。但是在这个学期，我会去认真改正，我会以最还得状态去面对新的学期。 还有，在初一我发现有许多人比我努力。在我们班上有一个人，她在初一的时候英语很不好，比我还差。但是后来她努力了，现在已经在中上了。她现在还是默默无闻地认真学习，她不懂感认真像我们请教。老师您多次叫我向她学习，这回我认识到了，你是对的，我早都有向她学习了。我有许多的坏习惯了，骄傲是我最大的弱点，我会改的——在这个学期。英语我一定会跟上去，相信我。 新的学期，我会有新的改变。老师请你相信，我会给您带来惊喜！
风水学入门讲义(1） 2009-03-17 19:12:54来自: 圆圆的圆(去云游吧~) 编者按：人类一直在追求和平的环境和健康的体魄，然而万事万物都是普遍联系的，一些看似不相干的事，却往往联系得如此紧密，大大超出了人类现有的认识水平。对于许多民族和国家的人来说，不会认为自身的健康和自己的住宅以及过世家人陵墓的方位、地势等问题有太大的关系。但在中国，这个问题已经历了数千年的验证。也许用全息论的观点，可以做出一点勉强的解释。 前言 中国传统文化浩如烟海，内容繁多，其中包含的“风水学”更显得博大精深，为中国炎黄子孙几千年来的经济、文化以及科技的发达，起着十分重要的作用。它像一颗灿烂的明珠，永放光芒。自古以来，人们用风水知识进行趋吉避凶、解除病苦和贫困，乞求子孙的繁荣、福禄的旺盛、仕途的顺昌、爵位的升迁等方面，均依赖选择、改变风水来实现。因此，古人总结为：风水之道“夺神功，改天命。”风水知识浩如烟海，笔者只能以所学之微不足道与各位同仁交流。 一、风水学的基本含义 二、谈风水学的起源及其历史发展 三、浅谈风水知识的基本内容 （一）形势 （二）理气 四、罗盘的发明和应用 五、风水对人体生命的影响 六、学习风水的现实意义 一、风水学的基本含义风水这一名字起始于中国晋朝托名为郭璞（公元276——324年）所著的《葬书》。
书曰：“葬者，乘生气也。”经曰：“气乘风则散，界水则止，古人聚之使不散，行之便有止。故谓之…风水?”。风水之法，“得水为上，藏风次之”。又曰：“浅深得乘，风水自成。夫阴阳之气，噫而为风，升而为云，降而为雨，行乎地中，而为生气。夫土者之体，有土斯有气，气者水之母，有气斯有水。”在这以前人们把风水文化称为堪舆学。《玄灵修真辞典》中说：“堪舆，属传统文化方术之一，堪舆风水之术，在我国民间流传较广，①天地的代称，《淮南子》许慎注：“堪，天道也。舆，地道也。”②即风水，指住宅基地或坟地形势，也指相宅、相墓之法。还可理解为：堪者仰观天象，舆者俯察地气。《辞源》说：“风水，指宅地或坟地的地势，方向等。”《辞海》说：“风水。也叫堪舆。……也指相宅、相墓之法。” 风水的核心内容，是人们对居住环境进行选择和处理的一种学问。风水理论的核心是人，它追求的目标是人与自然环境的和谐与统一。在天、地、人三者之间，既强调人的主体地位，又不偏废自然环境和地理环境，注重天时、地理、人和之间的协和自然美。故有人又称风水学是“地球磁场与人类关系的学问”，也有人称它为“美学”，还有人称它为“环境学”、“元素学”、“软科学”等等。其范围包括住宅、宫室、寺观、陵墓、村落、城市诸方面，其中涉及陵墓的称为阴宅，涉及人居住方面的称为阳宅。风水对居住环境的影响主要表现在：一是对基址的选择，即追求一种在生理和心理上都能满足的地形条件；二是对居住形态的处理，包括自然环境的利用与改造，房屋的朝向、位置、高低大小、出入口选择，道路、供水、排水等因素的安排；三是在上述基础上添加某种符号（即理气），以满足人们避凶就吉的心理需求（摘《风水探源序》）。风水学有时还叫地理风水或简称地理学，很早以前还有称其为青乌、青乌术的等等。 二、谈风水学的起源及其历史发展中国人对地理风水的意识产生很早，“上古之时，人民少而禽兽众，人民不胜禽兽虫蛇。”在那种危机四伏的自然条件下，人们先以树木为巢舍，后来在了解自然和改造自然的实践中，首先对居住环境进行了改造。大约在六七千年前的原始村落——半坡村遗址，“选址多位于发育较好的马兰阶地上，特别是河流交汇处……离河较远的，则多在泉近旁。”西安半坡遗址就座落于渭河的支流浐河阶地上方，地势高而平缓，土壤肥沃，适宜生活和开垦。由此可知，那时的部落对居住环境已有所选择，并且这种选择是建立在对地理环境和风水知识有所认识的基础之上，懂得居住方位与日照和风寒的关系。如半坡中间的房子朝东，围绕大房子氏族成员的房子多朝南，即表现出“喜东南，厌西北”的特点，遗址四周有防御性豪沟；沟北、南有公共墓地，居住与墓地分开（《风水探源》）。 到了殷周时期，已有卜宅之文。如周朝公刘率众由邰迁豳，他亲自勘察宅茔，“既景乃冈，相其阴阳，观其流泉。”（《诗经?公刘》）
初二新学期计划400字 导语：工作计划实际上有许多不同种类，它们不仅有时间长短之分，而且有范围大小之别。希望下面整理的初二新学期计划400字能够给大家带来一些帮助！ 新的学期即将到来，为了使下学期的学习成绩进步、各科成绩优异、不偏科，在此做新学期的打算，如下： 一、做好预习。预习是学好各科的第一个环节。 二、听课。学习每门功课，一个很重要的环节就是要听好课，听课应做到： 1、要有明确的学习目的; 2、听课要特别注重“理解”。 三、做课堂笔记。做笔记对复习、作业有好处，做课堂笔记应： 1、笔记要简明扼要; 2、课堂上做好笔记后，还要学会课后及时整理笔记。 四、做作业。 1、做作业之前，必须对当天所学的知识认真复习，理解其确切涵义，明确起适用条件，弄清运用其解题的步骤; 2、认真审题，弄清题设条件和做题要求; 3、明确解题思路，确定解题方法步骤; 4、认真仔细做题，不可马虎从事，做完后还要认真检
查; 5、及时总结经验教训，积累解题技巧，提高解题能力; 6、遇到不会做的题，不要急于问老师，更不能抄袭别人的作业，要在复习功课的基础上，要通过层层分析，步步推理，多方联系，理出头绪，要下决心独立完成作业; 7、像历史、地理、生物、政治这些需要背的科目，要先背再做。 五、课后复习。 1、及时复习; 2、计划复习; 3、课本、笔记和教辅资料一起运用; 4、提高复习质量。 做好以上五点是不容易的，那需要持之以恒，我决心做到。 送走了初一，我们又迎来了初二的新学期，在每一个全新的起点，我们每个人都会有一种期盼：明天的我要更精彩。因此，我们会更多地在对美好未来的憧憬中，展现着自己迎接新学期的新气象。相信在这种新气象的感染和鼓舞下。 首先，应该先纠正自己的学习态度。“态度决定一切!”心态是取得成功的一个非常关键的环节，拥有好的心态，就会拥有好的成绩! 在语文方面，我会做到课前预习，认识本课生字生词，
新学期的打算_新学期新计划作文800字 经过了一个月愉快的假期，我们又将回到熟悉的校园里和大家一起学习。为了让这个新的学期过得更好，比上个学期过得更精彩更“有味”，我决定制定一个“开学新计划”。 在上个学期中，平时情况、学习成绩以及课外的学习总的来说还算不错！其中考试语文成绩98、数学成绩89，英语成绩97。。语文我最拿手的科目还算不错，就是考试时没看清题目，不然就能和我日思夜想的100分“做朋友”了。数学成绩大大出乎我的意料之外，整张试卷全是大大的“×”，好像在嘲笑我似的。英语成绩还好，不是很差。到了期末考试，成绩的变化大了很多，语文94分，数学98分，英语100分。对比上次语文的成绩，明显退步了许多，应该都是基础题丢的分。数学和英语成绩却“逆袭”了。期中的数学89“变成”了98，虽说是98，但班上有几乎三分之一的同学那了100分，我应该更努力才是！英语的100分最让我欣喜若狂，看到试卷上的100分就像是让我吃了一块蜜糖一般，嘴里甜，心里更甜！想到语文的成绩，就像是妈妈说的一样：“不能骄傲！”语文的分数就是我太骄傲“以为”“我肯定能拿好成绩！”把考卷草草做了事，检查也是一眼望过去就不检查了。结果就这样“变成”94了！一个学期也让我体会到了许多！ 为了让这一个学期过得比上一个学期更好，我必须要好好约束自己。上课小话不能讲，老师讲课时要认真听，就上个学期的一个“不留神”100分就和我说“拜拜”了。每一次作业都要认真做好，争取
做到最好！考试还是平时测验都要认真对待，把知识点学到点学到位。除了学习，课外的活动也是必不可缺的，体育课多活动锻炼身体。有时间可以多看看书，把自己的知识量扩大。每天晚上睡前读些单词。相信把这些做好，这个学期应该就能愉快又轻松的度过！ 奋斗吧！为了迎接即将到来6年级的“挑战”。奋斗吧！为了期中期末考卷上那令自己满意的分数。奋斗吧！为了自己知识量的提高。奋斗吧！骚年！
新学期计划800字作文 新学期计划800字作文1、每天都不能松懈。上课要认真听讲，特别是老师分析题的时候，更要仔细听，不能心不在焉。把当天所学的课程复习一遍，还要把第二天要学的课程预习一遍。 2、坚持每天早早起床，读15分钟语文，15分钟英语。 3、认真完成作业，这样既能发现不足，还可以巩固知识。语文作业要字迹工整;数学作业不能马虎，写完后认真检查并验算;英语作业书写要规范。 4、尊老爱幼、尊敬师长、乐于助人，还要爱护环境，看见垃圾捡起来，扔到垃圾箱里…… “每天我都能看得见，时刻提醒自己要努力!”王宇鹏说。 新学期快到了。为自己的学习提前做好规划，明确新学期的目标，找到前进的方向，才能收获更大的成绩。 王宇鹏早早就设计着自己新学期计划，“我都想了一个假期了!先是总结了上学期的学习情况，寻找自己学习中的问题，把他们都写出来。再把自己想达到的目标都列举出来，最后找出可行的办法，综合成新学期计划。我就照着它严格要求自己，相信新学期一定会有进步的。”王宇鹏自信地看着新学期计划。“光说是没有用的，关键要看行动。”王宇鹏的父亲笑着说。“那你们就负责监督，看我的行动吧!”王宇
鹏信心十足，对新学期充满期待。 和王宇鹏一样，早早制定了新学期计划的张扬也期盼着快点开学。“我的计划很切合实际，每天的作息时间、学习时间、学习科目和游戏时间都规定好了，只要严格执行，就一定能有成效。我相信，在新学期，我的成绩一定会再提高的。”张扬的妈妈说，张扬每学期都会根据新学期情况制定出相应的学习计划，并严格按照上面的时间安排去做，所以张扬的成绩一直很好，“学习根本不用我们操心。”张扬的妈妈自豪地抱起女儿。 “快开学了，同学们在经过一个暑假以后，难免会有所松懈。为了尽早地进入学习状态，适应新学期的学习生活，能够提早制定学习计划，或者大致勾勒出新学期的规划，都会帮助学生回归学习状态，为开学打好基础，也能提高学生的学习效率，实现他们自己的目标。”长春市87中学的付蔷老师也在准备着她新学期的计划：检查作业，开班会欢迎大家回来，一起分享假期心得，谈谈自己的新目标……“我们都站在新的起点上，一起迎接开学，一起迎接明天的挑战。” 新学期计划800字作文学习目标明确，科学安排学习时间，有些学生学习却毫无计划。“脚踩西瓜皮，滑到哪里算哪里”，这是很不好的。高尔基说：“不知明天该做什么的人是不幸的。”有的学生认为，学习有教育计划，老师有教学计划，跟着老师走，按照学校走就行了，何必自己在定
初二学期总结800字 紧张又急促的考试结束了，剩下的就是反思和总结。对于初二这重要学期来说，一定要好好努力。这学期是一个重要过度。 自己很清楚，这次考试并不理想。回想我这一年的学习来说，没有多大的进步。语文这次题不是很难，但是，由于自己的基本功不扎实，知识没有记牢，有些很简单的问题也失分，实在不应该。英语也不难，但是有点马虎，失分了，与其他同学拉开距离。所以以后一定不能马虎，不能掉以轻心，否则后悔莫及。物理和数学相对简单一点，不过有些问题没弄清楚，也没来得及问老师。在考试时丢了分。至于历史之类的科目都是需要背记的东西，(如果没有考好，便是上课时没好好听讲，不知道重点在哪?虽背了很多，但大都不是重点，没有考。) 在考试时，不太相信自己，对于记忆模糊的，影响中是哪个，但是不相信自己，硬要选一个自己毫不知道的答案。这样的情况经常出现。以至于失分严重。当然，还要制定合理的学习计划： 1：平时要养成认真的习惯，做题仔细，保证写一个就对一个。 2：课上认真，注意听重点。 3：课下不懂的，虚心请教同学老师。 另外，积极阅读有关书籍和资料，扩大自己的知识面;经常提出问题，与同学讨论，向老师请教;用知识来武装自己的头脑，知识是无价的。首先，合理安排时间，调整好作息时间，分配好学习、工作、娱乐的时间。时间是搞好学习的前提与基础，效率和方法更为重要。尽管取得一些成绩，但离心中的目标还很远，仍需继续努力，抓期末总结 初2的第一个学期就这样结束了.迎来了盼望已久的寒假. 时光飞逝，斗转星移。转眼成为高一一班一员已半年多了。回首这半年的点点滴滴，朝朝暮暮，心中顿生了许多感触。这半年中经历的每一天，都已在我心中留下了永久的印记，因为这些印记见证我这样一个新生的成长。在过去半年的内，通过不断地学习，我收获了很多.时间就是这么无情头也不回的向前走着，而我们却在为了不被它丢下死命的追赶着。是的，谁都不想被时间丢下.而我们也随着时间的流逝一点一点的成长.而美好的纯真随着风雨的磨灭化成了成熟.或许这正是成长的代价.回想自己还是考生的那段日子，显得是那么的遥远。我在憧 憬中懂得了来之不易的珍惜;在思索中了解了酝酿已久的真理;在收获后才知道努力的甜美。突然觉得自己似乎明白了许多事情，但是仔细琢磨后又不尽然……原来过去所见所识都是那么的偏见而又肤浅，以前的天真似乎在一瞬间幻化成无知和可笑，我想谁又不是这样的呢?或许在以后也回嘲笑现在的渺协…我们不得不笑着回首我们所走过的路.：出勤情况：请了一次病假.拉下一天的课希望下学期争取做到全勤本学期没有迟到的情况。 在日常生活上：以前我是一个衣来伸手饭来张口的小孩子，而通过7天军训生活和这半年老师和同学们的帮助，使我养成了独立性，不再娇生惯养，现在我已经能做一些力所能及的家务了。 在学习上：我深知学习的重要性。面对二十一世纪这个知识的时代，面对知识就是力量，科学技术是第一生产力的科学论断，我认为离开了知识将是一个一无是处的废人。以资本为最重要生产力的资本家的时代将要过去，以知识为特征的知本家的时代即将到来。而中学时代是学习现代科学知识的黄金时代，中国的本科教育又是世界一流的，我应该抓住这个有利的时机，用知识来武装自己的头脑，知识是无价的。首先，合理安排时间，调整好作息时间，分配好学习、工作、娱乐的时间。时间是搞好学习的前提与基础，效率和方法更为重要。其次，要保质保量的完成老师布置的作业，老师布置的作业一般是她多年教学经验的总结，具有很高的价值，应认真完成。认真对待考试，考前认真复习。另外，积极阅读有关书籍和资料，扩大自己的知识面;经常提出问题，与同学讨论，向老师请教;搞好师生关系，师生相处
初二开学新学期计划五篇 一、做好预习。预习是学好各科的第一个环节，所以预习应做到： 1、粗读教材，找出这节与哪些旧知识有联系，并复习这些知识； 2、列写出这节的内容提要； 3、找出这节的重点与难点； 4、找出课堂上应解决的重点问题。 二、听课。学习每门功课，一个很重要的环节就是要听好课，听 课应做到： 1、要有明确的学习目的； 2、听课要特别注重“理解”。 三、做课堂笔记。做笔记对复习、作业有好处，做课堂笔记应： 1、笔记要简明扼要； 2、课堂上做好笔记后，还要学会课后及时整理笔记。 四、做作业。 1、做作业之前，必须对当天所学的知识认真复习，理解其确切涵义，明确起适用条件，弄清运用其解题的步骤； 2、认真审题，弄清题设条件和做题要求； 3、明确解题思路，确定解题方法步骤； 4、认真仔细做题，不可马虎从事，做完后还要认真检查； 5、及时总结经验教训，积累解题技巧，提高解题能力； 6、遇到不会做的题，不要急于问老师，更不能抄袭别人的作业， 要在复习功课的基础上，要通过层层分析，步步推理，多方联系，理 出头绪，要下决心独立完成作业； 7、像历史、地理、生物、政治这些需要背的科目，要先背再做。 五、课后复习。 1、及时复习；
2、计划复习； 3、课本、笔记和教辅资料一起运用； 4、提高复习质量。 做好以上五点是不容易的，那需要持之以恒，我决心做到。 新学期来到了，我站在了一个新的起点，面临着更大的挑战，我 深知，这条路不好走，在这条求学路上，我们面临的是更大的困难， 更高的难度。为此我也有一些想法。 其实我的愿望与大家一样，那就是要在新的学期里努力学习，认 真听老师讲课，上课要积极发言。不仅如此，还要经常参加各类活动，做到这些，还远远不够，我还希望自己能在每一次的考试上考出好成绩。我知道，要做到这些是十分艰难的的，但是只要你坚持到底，永 不退缩，那这些又将变得非常容易。 我们在学习中也是十分有乐趣的，看到这里你一定会不相信吧， 听我慢慢告诉你。 我们有很多好朋友，例如，在数学中，我们和众多的阿拉伯小朋 友拉着手，漫游数学中学习的乐趣；在语文中我们和中国的优美文字 一起欢闹；在英语里，我们对着ABC在笑；历史里，我们学习着古老 中国的故事……还有很多很多，这就是我们快乐的发源地，我们灵魂 的升华！ 我的理想就是将这些在初中所学的知识刻在我的脑子里，好让自 己今后的各科考试里取得优异的成绩。 我知道，每个人心里想的是不一样的，因为，人各有志，目标不同。如今我渐渐长大了，我的新目标不是做什么伟人，也不想当什么 艺人，更不是成为科学巨人。而是长大后考上一座较好的大学，找到 一份稳定的工作。在自己平凡的岗位上为国家，为社会作出不平凡的事，贡献出属于自己的一份力量，这就已经足够了。 让我们大家在新学期里携起手来，共同朝着自己的目标前进吧！ 大声喊一声：“新学期，我来了！”作文
风水入门基础知识 包头市传统文化发展促进会 一、罗盘的发明和应用 罗盘的最重要部分是指南针，指南针为中国四大发明之一。约四千余年前，黄帝战蚩尤，蚩尤放大雾以迷，黄帝无法辨别方向，幸得身边的风后将他多年研制成的指南针装入兵车之中，名为指南车，黄帝借此一举战败了蚩尤。意大利航海家哥伦布借助中国的指南针而发现了新大陆。 科学家发现，地球上每个空间或每个物体都存在不同质量的电磁波，它们有吉凶之别，对人体存在一定影响，若一个地方的电磁波与人的生命磁向或屋的磁波十分配合，为吉方；相反，两者发生排斥，即为凶方。古贤借助指南针测定地方的磁向，再配上时间的推算，便推出吉凶的方位。这样根据需要发明了罗盘。 罗盘一般为圆形，圆心装入指南针，红头一端指南，另一端指北。即用于子午线定位。然后将二十四字按八方位置平均刻入，即罗盘之地盘。用于立穴定向、格龙。后来发展为将先天八卦、后天八卦、六十四卦、六十龙透地、九星、黄泉煞、二十八宿、一百二十分金等内容装入，风水家用它来勘察阴阳二宅，已成为风水师唯一使用的工具。是经天纬地的仪器。罗盘目前主要有三合盘（即分正针、中针、缝针），还有三元卦盘，又叫挨星盘，装入先天六十四卦、三百八十四爻分度。罗盘已担负着风水师趋吉避凶的全部使命。 二、风水对人体生命的影响 择风水，是人们生活中必不可少的一项活动。只要是人，不管是活着的人，还是故去之人，均离不开风水的影响，它直接左右着人的寿数、健康、人事关系等等。不管你是在意还是无意，是相信还是不信，它都在对你起着作用。在卜卦预测中有一个基本原则，那就是：“心诚则灵”、“信则有，不信则无。”而风水则不一样，它不管你是心也好，心不诚也好，信亦好，不信亦好，它都客观存在着，只要你一住进房子，你就在这栋房子的保护下生存。阳宅的房子也有“生命”，它的生命强弱与吉凶，取决于周围环境、形态、坐向、通风、采光以及其“出生
新学期新计划作文800字 新学期新计划作文800字一：“开学了!开学了!”大家都背着新书包，穿着漂亮的衣裳，整齐的挂着红领巾，迎接新的学期。妈妈看见我那崭新的书包，桌上的文具盒和铅笔都整齐的摆放着，拍拍我的肩膀说：“现在是五年级的学生了，不要看外表美而是美，要心灵也美，这才是真正的美!” 爸爸走了过来，看见我那零零星星的暑假作业还瘫在桌上，有些还没有做完，对我说：“五年级是很关键的一课，一定要努力学习，当然，作业一定要按时完成!”我点了点头。在新的学期里，我一定要突破自己，记住爸爸妈妈的嘱咐，更要把自己以前的缺点改正过来，做一个好学生! 在上个学期里，我一直有偏科的现象，语文和英语成绩都很不错，可是数学成绩就不行了，常常考不好，就是因为考试的时候粗心大意，如果不是进位进错了，就是把0看成6，有时错的更离谱，把0。56看成05。0，真是没话说。我的应用题也老是犯老毛病，也是多了这个友漏了那个，应用题总是分数最多的题目，有时5、6分，有时10分的说不定，这样扣了分数，数学成绩能高么! 每当上数学可是，总是集中不了注意力，一看到数学就头痛，那密密麻麻的一大串数字，都烦死人了。可是妈妈说：
“光把语文学好是没有用的，一定要样样都好!”所以，我要把数学成绩赶上去，要做到3点：1。在家里多练练口算，这样口算会越来越快，准确率也会越来越高的!2。上课要集中注意力，不要一下子想这个，一下子想那个的，还有做试卷时要一题一题看清楚了在做，口算时草稿要多打几遍，不要把口算题都做错了!3。我可以每一本趣味数学看看，这样可以加大对数学的兴趣! 我相信，我只要认真做好这几点，数学成绩一定会提升的，不管做什么事都是一样的，不仅仅是数学，只要认真对待每一件事，不管多困难，你总能克服。让我们撕下一张张“丑脸蛋”，换上新的面孔，来迎接新的一天把! 新学期新计划作文800字二：一个漫长的寒假已过去,迎来的是一个美好的春天.在这万物复苏的时刻,新的一学期又开始了,迎着早晨的第一缕阳光,我们又重新回到美丽的校园,开始了新一学期的征途. 在过去的征程中,曾拥有过欢笑,拥有个阳光,这都已成为永远的记忆,现在加上一把锁将它封锁起来，摆在我们面前的一切都是新的,看呀?新的学期正在向我们招手.经过五年的锻炼,我们不都是一个个战士,抹去心灵的灰尘，拿上我们的武器,整装待发,新学期的目标向我们冲击! 我们又迎来了新学期，在每一个全新的起点，我们每个人都会有一种期盼：明天的我要更精彩。因此，我一定在对
初中新学期新计划作文800字 导读：篇一：新学期新计划作文 迷迷糊糊，忙忙碌碌，一个寒假又让它溜走了。迎来的，又是一个崭新的开始;崭新的学期，又和一位位同学们站在了同一起跑线上，准备在这一学期里，再次和自己认定的对手比个高低。 新的学期，新的开始，新的打算。在新的学年里，我们要会追求，追求成绩，追求学问，追求我们想得到的，追求是一种伟力。追求，使猿不安于现状最终走出了茹毛饮血的世界;追求使人历尽千难万险而无悔。左丘明失明著《左传》，司马迁遭宫刑写下史家绝唱。追求，使人为它粉身碎骨而无怨。商鞅将临分身之刑而谈笑自若，谭嗣同面对屠刀高呼“快哉，快哉!”虽前面有千沟万壑仍奋然东流;檐头雨珠依恋大地，明知会跌个粉碎，依旧投向大地;大雁心怀故园，千山万水也阻挡不了它归来……追求是一种伟力，永远存在于世间。 既是追求，又何必管它艰难险阻?只要一心一意的追求自己的目标，世界便永远常新。鲁迅先生赞叹第一个吃蟹的人是勇士，为人类发现了一种佳肴，这也是一种追求;达尔文周游世界，创立进化论学说，这也是一种追求。追求有大有小，但只要追求新生事物，追求真理，那么无论匹夫走卒，王侯公卿，他们都是平等的追求者。我们应该对于他们的追求永报以鼓励的笑颜。他们的精神也将超越身份、地位、时间、空间而流传下去。 追求的机会均等，可有人善于追求，有人不善于追求，有人追求
一生享乐，追求的肤浅;有人为学“孙康映雪”，白天游乐而夜晚读书，追求的虚伪;有人做事一曝十寒，追求无力。追求不是贪欲，不容人玷污;追求不是勋章，不为标榜;追求不是弹簧，可以随意屈伸，任人扭曲。既要追求，便要追求深远、博大，野心、贪心、私心永远与追求无缘。 追求到深处，那追求本身便化成了一种快乐。孔子礼乐，三月不知肉味;贝多芬双耳失聪，依然谱写出他无法亲耳听到的乐章;居里夫人获得的奖章可以做孩子的玩具。这一切一切，都说明追求已融入了伟人们的血液，不需什么回报，追求本身已给我们心灵以安慰。追求也会成“痴”。据说，曹雪芹披阅十载而终于写成了《红梦楼》，当朋友探望他时，见到他还在痴痴地吟读着。朋友唤他，曹公抬头茫然一笑，又低头吟读起来。这部血泪凝成的《红梦楼》已使曹雪芹深深的陶醉进去。追求是他为自己、为他人创造了一个世界。这其中的滋味，唯有追求者才能理解的最真切。这便是境界。追求的最高境界，不追求的人，一生永不会感受得到。 既然追求创造了世界，充实、丰富了世界，同样也能创造我们，充实、丰富了我们。那么，同学们，去追求吧，从现在开始! 篇二：新学期新计划作文 如果说生命也有季节，那年轻就是风华正茂的时光：如果说人生也有朝暮，那年轻就在太阳刚刚升起的地方。 今天，对于所有的同学来说，又是一个新的起点，年轻的你，要
平洋要诀 (杨公风水学术平洋秘诀) 山地属阴，平洋属阳，高起为阴，平坦为阳。阴阳各分，看法不同。山地贵坐实朝空，平洋要坐空朝满。山地以山作主，穴后宜高。 平洋以水作主，穴后宜低。书云：“山上龙神不下水，水里龙神不上山。”盖以地阴盛阳衰，只要龙真穴的，气脉雄壮，即水法稍差，初年亦发富贵。山上龙神不下水，此之谓出。平洋地阳盛阴衰，只要四面水绕归流一处，以水为龙脉，以水为护卫，若得水神合法，消纳大小文库，即龙穴不真，亦发富贵。水里龙神不上山，此之谓也。至于龙水配合二十四图，不论山地地平洋，一样作用。惟立向分坐实前空、坐空朝满耳。 平洋穴论 夫平洋者，一片皆水，而无山冈土岭。水动属阳，山静属阴。山地穴后先靠山，主丁旺寿高。平洋地则要枕水，方主丁旺寿高。书云：“风吹水激寿丁长。”盖坐水如山，坐水允宜反弓，如山之倒地。平洋地多无起伏，凡是周围皆水，中间微高，水不到者，即是平洋一突，始为奇穴。即落于高处，百前水绕抱归库，水外正向前，要一层高似一层，大发富贵。书云：“平洋明堂低外高，长幼两房足富铙。”又云：“平洋明堂高外高，金银积库米阵廒。”故穴前之水要眼弓水，外高则富贵，外低则败绝。书云：“明堂低又低，万两黄金也化灰。”又云：“明堂低又低，子孙穷到底。”至于平洋地贵人方缺陷，则宜补修。 或土堆、房屋、庙宇、高塔，主速发科甲，盖平洋缺故也。平洋龙最难识认，故云：“平洋莫问龙。水绕是真迹。”水自左来龙则从左，水自右来龙则从右。两水相近处。即是过峡、束气。 两水交合处，即是结作。龙分阴阳两片，取水对三义细认踪，此看平洋之妙诀也。平洋地止产墓家堆高明、后不宜修土圈围，故有则不利于丁。若四面无水，止是一片平地。即以乾流为水，以路为砂。 低者为水，高者为破。以村庄、乡场、市郡、房屋、庙宇、城墙等作 1
新学期计划800字 追求：是充实生命的必然途径。时间就像那无情的流水，流去我在小学里的五年光阴。转眼寒假又将成为过去，新的学期即将来临。仔细的回想，过去的五年里，自己到底做了什么?可是，什么也想不 起来。难道真要这样，流给母校的是一片空白记忆吗?不，我希望， 在小学生活的最后半年里要踏踏实实的做好每一件事。在这半年里 我一定要珍惜每一秒时间，争取不让自己有荒度的一刻。 我想，人的希望是会随时改变的，不同的时间，不同的情况下，我们每个人的希望也是不同的。在新学期里，我的希望是永远记住 母校，因为这毕竟是我生活了六年的小学啊!同时，我也希望能在这 最后的半年里为我亲爱的母校做一点事情，留下一些美好的东西。 我想，我一定会用实际行动来做这件事的。人在一生中，不管他成 为了什么，是名人，或是伟人，还是极平凡的普通人，都会永远记 住自己的母校，都对他有着非常深刻的感情，我也要做这样的人。 我知道，每个人心里想的是不一样的，因为，人各有志，目标不同。如今我渐渐长大了，我的新目标不是做什么伟人，也不想当什 么艺人，更不是成为科学巨人。而是长大后考上一座较好的大学， 找到一份稳定的工作。在自己平凡的岗位上为国家，为社会作出不 平凡的事，贡献出属于自己的一份力量，这就已经足够了。 新学期里人人都有新打算，你一定也和我一样吧!让我们大家在 新学期里携起手来，共同朝着自己的目标前进吧。 一个新的学年已经开始了，我要把所有的精力用到学习中去。 3.每天坚持朗读外语作品，养成良好的语感。认真识记考纲后面的单词，严格避免中考因单词而失分。认真复习和预习全品的考点 聚焦和附录，要求重点掌握语法，句型。注意活学活用。 以上的学习计划要认真实施，成功在于行动，过一段时间要仔细分析检查现状，与制定的计划。我要确定学习的目标，努力学习的
初三新学期计划随笔作文8篇 新的学期又开始了，在这个学校我已经经历了四次开学，这次开学会是最后一次，也会是最难忘的一次。这是我初中校园的最后一年，我很快就要毕业了，离开我的初中校园。 我不在是初一、初二的小孩子了。在初四我有很多压力，面对这些压力我们应怎样面对？微笑着面对，努力做好每一件事。其实压力也是一种动力，推动着我们努力向上。 我们又迎来了一个新的春天，新的自己！听见楼下军训的同学，我便想起那时的我。那么迷茫、那么无知、那么幼稚。时光真的匆匆如流，那时我才上初一，好想一眨眼地时间我上了初四。初四是最后一年也是最重要的一年。我要认真对待最后一年，努力拼搏！作业要认真完成，课上要认真听讲。很快我就要离开校园，走新的校园。最后一年我要好好珍惜！不浪费一分一秒。 想到还有不到一年的时间就离开我的初中校园，真的很不舍。最后一年我要拼了，努力学习迎接新的挑战。努力拼搏，赢取一片阳光！ 新学期的脚步已然来临。回首来时路，才发现上学期的帷幕早已落下，来时那风雨侵蚀的泥泞小路也早已蒙上了一层浓厚的面纱。再多的悔恨亦不能回到过去，那就放下悔恨吧！重新站在起跑线上，迎接挑战，永不言弃。因为失败只
能用成功来洗刷耻辱。 新的学期，新的开始，我正努力编织着梦的蓝图，朝希望的彼岸乘风破浪。但来时的旅途，我走得那样艰辛，一次又一次的跌倒使我身心俱疲，但这并不足以让我退缩。只会让我看清自己的弱点，贝多芬说：“卓越人的一大优点是在不利与艰辛的遭遇里百折不挠。”我并不是一个卓越的人，但我会努力使自己成为一个卓越的人。 我相信“世上无难事，只怕有心人。”“精诚所致，金石为开。”只要不懈努力，我一定会攻克那些障碍，我坚信成功是属于有梦想的人。放飞我的梦想，我相信它正展翅遨翔冲向去霄…… 马上就要开学了，开学后我就是初三的学生了。初三的到来，好像自己马上就要上战场了。自己慢慢地有一点危机感了，害怕同学超越自己。为了能在初三学的好一点，我决定现在就想好新学期的打算。 第一，我决定学会放弃。在前两个学期里，我经常去上网、看小说、睡觉，对于老师布置的作业也不认真完成。对于这些流失的时间，我不能挽回，对于初三我一定要把握。初三我要学会放弃，放弃了我也可能失去很多东西。但是我不怕，放弃了会让我更认真点。那些东西以后来做也一样，何必就要现在呢？我对自己说。 第二，我决定抓不足。在我的成绩中是不平均的，分数
初二新学期打算作文（1）： 一个漫长的暑假已过去，迎来的是一个完美的秋天。在这丹桂飘香，金秋收获的季节里，新的一学期又开始了，迎着早晨的第一缕阳光，我们又重新回到美丽的校园，开始了新一学期的征途。 我已踏进了初中的殿堂，在初中的生活中要百尺竿头，更进一步。在这时期里，我要认真，仔细地规划每一分钟。认真投入到学习中。以前有一位老师对我说，态度决定一切，要以良好的态度去应对学习。挑战自我，相信自我，我个人认为，人一生的时光是有限的，时光不等人，因为这是我初中生涯的刚开始一段时光，我不会放过从我身边中的每一份时光，挣取把握好身边的每分每秒。我要为我的初中学习生活定个目标。 我们需要一步一个脚印，踏踏实实地学习，书山有路勤为径，学海无涯苦作舟。知识是珍贵宝石的结晶，文化是宝石放出的光泽。让我们共同探索未知世界，向着自我的目标，奋力前进！ 弗兰克林说过：有十分之胆识，始可做十分之事业对于我们中学生来说写一篇好的作文画一幅美丽的图画唱一支动人的歌曲打一场漂亮的球赛都是我们中学征途中的一次次成功。成功中是我们的喜悦，成功背后是我们辛勤的汗水，没有耕耘就哪有收获没有付出哪有所得。因此，对于即将成熟的我们，更就应好好珍惜这段宝贵的时光充实自我，把自我的理想变为势不可当的动力！ 新学期来到了，我站在了一个新的起点，面临着更大的挑战，我深知，这条路不好走，在这条求学路上，我们面临的是更大的困难，更高的难度。为此我也有一些想法。 其实我的愿望与大家一样，那就是要在新的学期里努力学习，认真听老师讲课，上课要用心发言。不仅仅如此，还要经常参加各类活动，做到这些，还远远不够，我还期望自我能在每一次的考试上考出好成绩。我明白，要做到这些是十分艰难的的，但是只要你坚持到底，永不退缩，那这些又将变得十分容易。 我们在学习中也是十分有乐趣的，看到那里你必须会不相信吧，听我慢慢告诉你。 我们有很多好朋友，例如，在数学中，我们和众多的阿拉伯小朋友拉着手，漫游数学中学习的乐趣;在语文中我们和中国的优美文字一齐欢闹;在英语里，我们对着abc在笑;