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Android Recipes A Problem-Solution Approach Dave Smith
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To the Almighty, who guides me in every aspect of my life. And to my mother, Smt. Savitri Mishra, and my lovely wife, Smt. Smita Rani Pathak.
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About the Author
Raju Kumar Mishra has a strong interest in data science and systems that have the capability of handling large amounts of data and operating complex mathematical models through computational programming. He was inspired to pursue a Master of Technology degree in computational sciences from the Indian Institute of Science in Bangalore, India. Raju primarily works in the areas of data science and its various applications. Working as a corporate trainer, he has developed unique insights that help him in teaching and explaining complex ideas with ease. Raju is also a data science consultant who solves complex industrial problems. He works on programming tools such as R, Python, scikit-learn, Statsmodels, Hadoop, Hive, Pig, Spark, and many others.
About the Technical Reviewer
Sundar Rajan Raman is an artificial intelligence practitioner currently working for Bank of America. He holds a Bachelor of Technology degree from the National Institute of Technology in India. Being a seasoned Java and J2EE programmer, he has worked at companies such as AT&T, Singtel, and Deutsche Bank. He is a messaging platform specialist with vast experience on SonicMQ, WebSphere MQ, and TIBCO software, with respective certifications. His current focus is on artificial intelligence, including machine learning and neural networks. More information is available at https://in.linkedin.com/pub/sundarrajan-raman/7/905/488
I would like to thank my wife, Hema, and my daughter, Shriya, for their patience during the review process.
Acknowledgments
My heartiest thanks to the Almighty. I also would like to thank my mother, Smt. Savitri Mishra; my sisters, Mitan and Priya; my cousins, Suchitra and Chandni; and my maternal uncle, Shyam Bihari Pandey; for their support and encouragement. I am very grateful to my sweet and beautiful wife, Smt. Smita Rani Pathak, for her continuous encouragement and love while I was writing this book. I thank my brother-in-law, Mr. Prafull Chandra Pandey, for his encouragement to write this book. I am very thankful to my sisters-in-law, Rinky, Reena, Kshama, Charu, Dhriti, Kriti, and Jyoti for their encouragement as well. I am grateful to Anurag Pal Sehgal, Saurabh Gupta, Devendra Mani Tripathi, and all my friends. Last but not least, thanks to Coordinating Editor Sanchita Mandal, Acquisitions Editor Celestin Suresh John, and Development Editor Laura Berendson at Apress; without them, this book would not have been possible.
Introduction
This book will take you on an interesting journey to learn about PySpark and big data through a problem-solution approach. Every problem is followed by a detailed, step-by-step answer, which will improve your thought process for solving big data problems with PySpark. This book is divided into nine chapters. Here’s a brief description of each chapter:
Chapter 1, “The Era of Big Data, Hadoop, and Other Big Data Processing Frameworks,” covers many big data processing tools such as Apache Hadoop, Apache Pig, Apache Hive, and Apache Spark. The shortcomings of Hadoop and the evolution of Spark are discussed. Apache Kafka is explained as a publish-subscribe system. This chapter also sheds light on HBase, a NoSQL database.
Chapter 2, “Installation,” will take you to the real battleground. You’ll learn how to install many big data processing tools such as Hadoop, Hive, Spark, Apache Mesos, and Apache HBase.
Chapter 3, “Introduction to Python and NumPy,” is for newcomers to Python. You will learn about the basics of Python and NumPy by following a problem-solution approach. Problems in this chapter are data-science oriented.
Chapter 4, “Spark Architecture and the Resilient Distributed Dataset,” explains the architecture of Spark and introduces resilient distributed datasets. You’ll learn about creating RDDs and using data-analysis algorithms for data aggregation, data filtering, and set operations on RDDs.
Chapter 5, “The Power of Pairs: Paired RDD,” shows how to create paired RDDs and how to perform data aggregation, data joining, and other algorithms on these paired RDDs.
Chapter 6, “I/O in PySpark,” will teach you how to read data from various types of files and save the result as an RDD.
Chapter 7, “Optimizing PySpark and PySpark Streaming,” is one of the most important chapters. You will start by optimizing a page-rank algorithm. Then you’ll implement a k-nearest neighbors algorithm and optimize it by using broadcast variables provided by the PySpark framework. Learning PySpark Streaming will finally lead us into integrating Apache Kafka with the PySpark Streaming framework.
Chapter 8, “PySparkSQL,” is paradise for readers who use SQL. But newcomers will also learn PySparkSQL in order to write SQL-like queries on DataFrames by using a problem-solution approach. Apart from DataFrames, we will also implement the graph algorithms breadth-first search and page rank by using the GraphFrames library.
Chapter 9, “PySpark MLlib and Linear Regression,” describes PySpark’s machinelearning library, MLlib. You will see many recipes on various data structures provided by PySpark MLlib. You’ll also implement linear regression. Recipes on lasso and ridge regression are included in the chapter.
The Era of Big Data, Hadoop, and Other Big Data Processing Frameworks
When I first joined Orkut, I was happy. With Orkut, I had a new platform enabling me get to know the people around me, including their thoughts, their views, their purchases, and the places they visited. We were all gaining more knowledge than ever before and felt more connected to the people around us. Uploading pictures helped us share good ideas of places to visit. I was becoming more and more addicted to understanding and expressing sentiments. After a few years, I joined Facebook. And day by day, I was introduced to what became an infinite amount of information from all over world. Next, I started purchasing items online, and I liked it more than shopping offline. I could easily get a lot of information about products, and I could compare prices and features. And I wasn’t the only one; millions of people were feeling the same way about the Web.
More and more data was flooding in from every corner of the world to the Web. And thanks to all those inventions related to data storage systems, people could store this huge inflow of data.
More and more users joined the Web from all over the world, and therefore increased the amount of data being added to these storage systems. This data was in the form of opinions, pictures, videos, and other forms of data too. This data deluge forced users to adopt distributed systems. Distributed systems require distributed programming. And we also know that distributed systems require extra care for fault-tolerance and efficient algorithms. Distributed systems always need two things: reliability of the system and availability of all its components.
Apache Hadoop was introduced, ensuring efficient computation and fault-tolerance for distributed systems. Mainly, it concentrated on reliability and availability. Because Apache Hadoop was easy to program, many people became interested in big data. Big data became a popular topic for discussion everywhere. E-commerce companies wanted to know more about their customers, and the health-care industry was interested in gaining insights from the data collected, for example. More data metrics were defined. More data points started to be collected.
Many open source big data tools emerged, including Apache Tez and Apache Storm. This was also a time that many NoSQL databases emerged to deal with this huge data inflow. Apache Spark also evolved as a distributed system and became very popular during this time. In this chapter, we are going to discuss big data as well as Hadoop as a distributed system for processing big data. In covering the components of Hadoop, we will also discuss Hadoop ecosystem frameworks such as Apache Hive and Apache Pig. The usefulness of the components of the Hadoop ecosystem is also discussed to give you an overview. Throwing light on some of the shortcomings of Hadoop will give you background on the development of Apache Spark. The chapter will then move through a description of Apache Spark. We will also discuss various cluster managers that work with Apache Spark. The chapter wouldn’t be complete without discussing NoSQL, so discussion on the NoSQL database HBase is also included. Sometimes we read data from a relational database management system (RDBMS); this chapter discusses PostgreSQL.
Big Data
Big data is one of the hot topics of this era. But what is big data? Big data describes a dataset that is huge and increasing with amazing speed. Apart from this volume and velocity, big data is also characterized by its variety of data and veracity. Let’s explore these terms—volume, velocity, variety, and veracity—in detail. These are also known as the 4V characteristics of big data, as illustrated in Figure 1-1.
Figure 1-1. Characteristcis of big data
Volume
The volume specifies the amount of data to be processed. A large amount of data requires large machines or distributed systems. And the time required for computation will also increase with the volume of data. So it’s better to go for a distributed system, if we can parallelize our computation. Volume might be of structured data, unstructured data,
or any data. If we have unstructured data, the situation becomes more complex and computing intensive. You might wonder, how big is big? What volume of data should be classified as big data? This is again a debatable question. But in general, we can say that an amount of data that we can’t handle via a conventional system can be considered big data.
Velocity
Every organization is becoming more and more data conscious. A lot of data is collected every moment. This means that the velocity of data—the speed of the data flow and of data processing—is also increasing. How will a single system be able to handle this velocity? The problem becomes complex when we have to analyze a large inflow of data in real time. Each day, systems are being developed to deal with this huge inflow of data.
Variety
Sometimes the variety of data adds enough complexity that conventional data analysis systems can’t analyze data well. What do we mean by variety? You might think data is just data. But this is not the case. Image data is different from simple tabular data, for example, because of the way it is organized and saved. In addition, an infinite number of file systems are available, and every file system requires a different way of dealing with it. Reading and writing a JSON file, for instance, will be different from the way we deal with a CSV file. Nowadays, a data scientist has to handle a combination of these data types. The data you are going to deal with might be a combination of pictures, videos, and text. This variety of data makes big data more complex to analyze.
Veracity
Can you imagine a logically incorrect computer program resulting in the correct output? Of course not. Similarly, data that is not accurate is going to provide misleading results. The veracity of data is one of the important concerns related to big data. When we consider the condition of big data, we have to think about any abnormalities in the data.
Hadoop
Hadoop is a distributed and scalable framework for solving big data problems. Hadoop, developed by Doug Cutting and Mark Cafarella, is written in Java. It can be installed on a cluster of commodity hardware, and it scales horizontally on distributed systems. Easy to program Inspiration from Google research paper Hadoop was developed. Hadoop’s capability to work on commodity hardware makes it cost-effective. If we are working on commodity hardware, fault-tolerance is an inevitable issue. But Hadoop provides a faulttolerant system for data storage and computation, and this fault-tolerant capability has made Hadoop popular.
Hadoop has two components, as illustrated in Figure 1-2. The first component is the Hadoop Distributed File System (HDFS). The second component is MapReduce. HDFS is for distributed data storage, and MapReduce is for performing computation on the data stored in HDFS.
MapReduce
HDFS
HDFS is used to store large amounts of data in a distributed and fault-tolerant fashion. HDFS is written in Java and runs on commodity hardware. It was inspired by a Google research paper about the Google File System (GFS). It is a write-once and read-manytimes system that’s effective for large amounts of data.
HDFS comprises two components: NameNode and DataNode. These two components are Java daemon processes. A NameNode, which maintains metadata of files distributed on a cluster, works as the master for many DataNodes. HDFS divides a large file into small blocks and saves the blocks on different DataNodes. The actual file data blocks reside on DataNodes.
HDFS provides a set of Unix shell-like commands to deal with it. But we can use the Java file system API provided by HDFS to work at a finer level on large files. Faulttolerance is implemented by using replications of data blocks.
We can access the HDFS files by using a single-thread process and also in parallel. HDFS provides a useful utility, distcp, which is generally used to transfer data in parallel from one HDFS system to another. It copies data by using parallel map tasks. You can see the HDFS components in Figure 1-3.
Hadoop HDFS
Figure 1-2. Hadoop components
MapReduce
The Map-Reduce model of computation first appeared in a Google research paper. This research paper was implemented in Hadoop as Hadoop’s MapReduce. Hadoop’s MapReduce is the computation engine of the Hadoop framework, which performs computations on the distributed data in HDFS. MapReduce is horizontally scalable on distributed systems of commodity hardware. It also scales for large problems. In MapReduce, the solution is broken into two phases: the map phase and the reduce phase. In the map phase, a chunk of data is processed, and in the reduce phase, an aggregation or a reduction operation is run on the result of the map phase. Hadoop’s MapReduce framework is written in Java.
MapReduce uses a master/slave model. In Hadoop 1, this map-reduce computation was managed by two daemon processes: Jobtracker and Tasktracker. Jobtracker is a master process that deals with many Tasktrackers. There’s no need to say that Tasktracker is a slave to Jobtracker. But in Hadoop 2, Jobtracker and Tasktracker were replaced by YARN.
Because we know that Hadoop’s MapReduce framework is written in Java, we can write our MapReduce code by using an API provided by the framework and programmed in Java. The Hadoop streaming module gives further power so that a person knowing another programming language (such as Python or Ruby) can program MapReduce. MapReduce algorithms are good for many algorithms. Many machine-learning algorithms are implemented as Apache Mahout. Mahout used to run on Hadoop as Pig and Hive.
But MapReduce wasn’t very good for iterative algorithms. At the end of every Hadoop job, MapReduce will save the data to HDFS and read it back again for the next job. We know that reading and writing data to a file is one of the costliest activities. Apache Spark mitigated this shortcoming of MapReduce by providing in-memory data persisting and computation.
Figure 1-3. Components of HDFS
■ Note You can read more about mapreduce and mahout at the following web pages:
The world of computer science is one of abstraction. Everyone knows that all data ultimately exists in the form of bits. Programming languages such as C enable us to avoid programming in machine-level language. The C language provides an abstraction over machine and assembly language. More abstraction is provided by other high-level languages. Structured Query Language (SQL) is one of the abstractions. SQL is widely used all over the world by many data modeling experts. Hadoop is good for analysis of big data. So how can a large population knowing SQL utilize the power of Hadoop computational power on big data? In order to write Hadoop’s MapReduce program, users must know a programming language that can be used to program Hadoop’s MapReduce.
In the real world, day-to-day problems follow patterns. In data analysis, some problems are common, such as manipulating data, handling missing values, transforming data, and summarizing data. Writing MapReduce code for these day-to-day problems is head-spinning work for a nonprogrammer. Writing code to solve a problem is not a very intelligent thing. But writing efficient code that has performance scalability and can be extended is something that is valuable. Having this problem in mind, Apache Hive was developed at Facebook, so that general problems can be solved without writing MapReduce code.
According to the Hive wiki, “Hive is a data warehousing infrastructure based on Apache Hadoop.” Hive has its own SQL dialect, which is known as Hive Query Language (abbreviated as HiveQL or HQL). Using HiveQL, Hive can query data in HDFS. Hive can run not only on HDFS, but also on Spark and other big data frameworks such as Apache Tez.
Hive provides the user an abstraction that is like a relational database management system for structured data in HDFS. We can create tables and run SQL-like queries on them. Hive saves the table schema in an RDBMS. Apache Derby is the default RDBMS, which is shipped with the Apache Hive distribution. Apache Derby has been fully written in Java; this open source RDBMS comes with the Apache License, Version 2.0.
Figure 1-4. Code execution flow in Apache Hive
1 ■ the era of Big Data, haDoop, anD
HiveQL commands are transformed into Hadoop’s MapReduce code, and then it runs on Hadoop cluster. You can see the Hive command execution flow in Figure 1-4. A person knowing SQL can easily learn Apache Hive and HiveQL and can use the benefits of storage and the computation power of Hadoop in their day-to-day data analysis of big data. HiveQL is also supported by PySparkSQL. We can run HiveQL commands in PySparkSQL. Apart from executing HiveQL queries, we can also read data from Hive directly to PySparkSQL and write results to Hive.
■ Note You can read more about hive and the apache Derby rDBms at the following web pages:
Apache Pig is data-flow framework for performing data-analysis algorithms on huge amounts of data. It was developed by Yahoo!, open sourced to the Apache Software Foundation, and is now available under the Apache License, Version 2.0. The pig programming language is a Pig Latin scripting language. Pig is loosely connected to Hadoop, which means that we can connect it to Hadoop and perform analysis. But Pig can be used with other tools such as Apache Tez and Apache Spark.
Apache Hive is used as reporting tool, whereas Apache Pig is used as an extract, transform, and load (ETL) tool. We can extend the functionality of Pig by using user-defined functions (UDFs). User-defined functions can be written in many languages, including Java, Python, Ruby, JavaScript, Groovy, and Jython.
Apache Pig uses HDFS to read and store the data, and Hadoop’s MapReduce to execute the data-science algorithms. Apache Pig is similar to Apache Hive in using the Hadoop cluster. As Figure 1-5 depicts, on Hadoop, Pig Latin commands are first transformed into Hadoop’s MapReduce code. And then the transformed MapReduce code runs on the Hadoop cluster.
Run on Hadoop Cluster
Figure 1-5. Code execution flow in Apache Pig
The best part of Pig is that the code is optimized and tested to work for day-to-day problems. A user can directly install Pig and start using it. Pig provides a Grunt shell to run interactive Pig commands, so anyone who knows Pig Latin can enjoy the benefits of HDFS and MapReduce, without knowing an advanced programming language such as Java or Python.
■ Note You can read more about apache pig at the following sites: http://pig.apache.org/docs/ https://en.wikipedia.org/wiki/Pig_(programming_tool) https://cwiki.apache.org/confluence/display/PIG/Index
Apache Kafka
Apache Kafka is a publish-subscribe, distributed messaging platform. It was developed at LinkedIn and later open sourced to the Apache Foundation. It is fault-tolerant, scalable, and fast. A message, in Kafka terms, is the smallest unit of data that can flow from a producer to a consumer through a Kafka server, and that can be persisted and used at a later time. You might be confused about the terms producer and consumer. We are going to discuss these terms soon. Another key term we are going to use in the context of Kafka is topic. A topic is stream of messages of a similar category. Kafka comes with a built-in API, which developers can use to build their applications. We are the ones who define the topic. Now let’s discuss the three main components of Apache Kafka.
Producer
A Kafka producer produces the message to a Kafka topic. It can publish data to more than one topic.
Broker
The broker is the main Kafka server that runs on a dedicated machine. Messages are pushed to the broker by the producer. The broker persists topics in different partitions, and these partitions are replicated to different brokers to deal with faults. The broker is stateless, so the consumer has to track the message it has consumed.
Consumer
A consumer fetches messages from the Kafka broker. Remember, it fetches the messages; the Kafka broker doesn’t push messages to the consumer; rather, the consumer pulls data from the Kafka broker. Consumers are subscribed to one or more topics on the Kafka
broker, and they read the messages. The consumer also keeps tracks of all the messages that it has already consumed. Data is persisted in a broker for a specified time. If the consumer fails, it can fetch the data after its restart.
Figure 1-6 explains the message flow of Apache Kafka. The producer publishes a message to the topic. Then the consumer pulls data from the broker. In between publishing and pulling, the message is persisted by the Kafka broker.
Producer Consumer Broker
Figure 1-6. Apache Kafka message flow
We will integrate Apache Kafka with PySpark in Chapter 7, which discusses Kafka further.
■ Note You can read more about apache kafka at the following sites:
https://kafka.apache.org/documentation/
https://kafka.apache.org/quickstart
Apache Spark
Apache Spark is a general-purpose, distributed programming framework. It is considered very good for iterative as well as batch processing of data. Developed at the AMPLab at the University of California, Berkeley, Spark is now open source software that provides an in-memory computation framework. On the one hand, it is good for batch processing; on the other hand, it works well with real-time (or, better to say, near-real-time) data. Machine learning and graph algorithms are iterative. Where Spark do magic. According to its research paper, it is approximately 100 times faster than its peer, Hadoop. Data can be cached in memory. Caching intermediate data in iterative algorithms provides amazingly fast processing speed. Spark can be programmed with Java, Scala, Python, and R. If anyone is considering Spark as an improved Hadoop, then to some extent, that is fine in my view. Because we can implement a MapReduce algorithm in Spark, Spark uses the benefit of HDFS; this means Spark can read data from HDFS and store data to HDFS too, and Spark handles iterative computation efficiently because data can be persisted in memory. Apart from in-memory computation, Spark is good for interactive data analysis.
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now got clear of the crowd, and no longer fearing to see the gallows, she ventured to lift up her eyes and look around. The tallness and massiveness of the buildings, some of which bore the cross of the Knights Templar on their pinnacles, while others seemed to be surmounted or overtopped by still taller edifices beyond, impressed her imagination; and the effect was rendered still more striking by the countless human figures which crowded the windows, and even the roofs of the houses, all alike bending their attention, as she thought, towards herself. The scene before her looked like an amphitheatre filled with spectators, while she and Richard seemed as the objects upon the arena. The thought caused her to hurry on, and she soon found herself in a great measure screened from observation by the overhanging projections of the narrower part of the West Bow, which she now entered.
With slow and difficult, but stately and graceful steps, she then proceeded, till she reached the upper angle of the street, where a novel and unexpected scene awaited her. A sound like that of rushing waters seemed first to proceed from the part of the street still concealed from her view, and presently appeared round the angle, the first rank of an impetuous crowd, which, rushing downward with prodigious force, would certainly have overwhelmed her delicate form, had she not dexterously avoided them, by stepping aside upon a projecting stair, to which Richard also sprung just in time to save himself from a similar fate. From this place of safety, which was not without its own crowd of children, women, and sage-looking elderly mechanics, with Kilmarnock cowls, they in the next moment saw the massive mob rush past, like the first wave of a flood, bearing either along or down everything that came in their way. Immediately after, but at a more deliberate pace, followed a procession of figures, which struck the heart of Lady Jean with as heavy a sense of sorrow as the crowd had just impressed with terror and surprise. First came a small company of the veterans of the city-guard, some of whom had perhaps figured in the campaigns of Middleton and Montrose, and whose bronzed, inflexible faces bore on this melancholy occasion precisely the same expression which they ordinarily exhibited on the joyful one of attending the magistrates at the drinking of the King’s health on the 29th of May.
Behind these, and encircled by some other soldiers of the same band, appeared two figures of a different sort. One of them was a young-looking, but pale and woe-worn man, the impressive wretchedness of whose appearance was strikingly increased by the ghastly dress which he wore. He was attired from head to foot in a white shroud, such as was sometimes worn in Scotland by criminals at the gallows, but which was, in the present instance, partly assumed as a badge of innocence. The excessive whiteness and emaciation of his countenance suited well with this dismal apparel, and, with the wild enthusiasm that kindled in his eyes, gave an almost supernatural effect to the whole scene, which rather resembled a pageant of the dead than a procession of earthly men. He was the only criminal: the person who walked by his side, and occasionally supported his steps, being, as the crowd whispered around, with many a varied expression of sympathy—his father. The old man had the air of a devout Presbyterian, with harsh, intelligent features, and a dress which bespoke his being a countryman of the lower rank. According to the report of the bystanders, he had educated this, his only son, for the unfortunate Church of Scotland, and now attended him to the fate which his talents and violent temperament had conspired to draw down upon his head. If ever he felt any pride in the popular admiration with which his son was honoured, no traces of such a sentiment now appeared. On the contrary, he seemed humbled to the very earth with sorrow; and though he had perhaps contemplated the issue, now about to take place, with no small portion of satisfaction, so long as it was at a distance and uncertain, the feelings of a father had evidently proved too much for his fortitude, when the event approached in all its dreadful reality. The emotions perceptible in that rough and rigid countenance were the more striking, as being so much at variance with its natural and characteristic expression; and the tear which gathered in his eye excited the greater commiseration, in so far as it seemed a stranger there. But the hero and heroine of our tale had little time to make observations on this piteous scene, for the procession passed quickly on, and was soon beyond their sight. When it was gone, the people of the Bow, who seemed accustomed to such sights, uttered various expressions of pity, indignation, and horror, according to their respective feelings, and then slowly retired
to their dens in the stairs and booths which lined the whole of this ancient and singular street.
Lady Jean, whose beautiful eyes were suffused with tears at beholding so melancholy a spectacle, was then admonished by her attendant to proceed. With a heart deadened to all sensations of wonder and delight, she moved forward, and was soon ushered into the place called the Lawnmarket, then perhaps the most fashionable district in Edinburgh, but the grandeur and spaciousness of which she beheld almost without admiration. The scene here was, however, much gayer, and approached more nearly to her splendid preconceptions of the capital than any she had yet seen. The shops were, in her estimation, very fine, and some of the people on the street were of that noble description of which she had believed all inhabitants of cities to be. There was no crowd on the street, which, therefore, afforded room for the better display of her stately and beautiful person; and as she walked steadily onwards, still “ushed” (for such was then the phrase) by her handsome and noble-looking attendant, a greater degree of admiration was excited amongst the gay idlers whom she passed, than even that which marked her progress through the humbler crowd of the Grassmarket. Various noblemen, in passing towards their homes in the Castle Hill, lifted their feathered hats and bowed profoundly to the lovely vision; and one or two magnificent dames, sweeping along with their long silk trains borne up by liverymen, stared at or eyed askance the charms which threw their own so completely into shade. By the time Lady Jean arrived at the bottom of the Lawnmarket, that is to say, where it was partially closed up by the Tolbooth, she had in a great measure recovered her spirits, and found herself prepared to enjoy the sight of the public buildings, which were so thickly clustered together at this central part of the city.
She was directed by Richard to pass along the narrow road which then led between the houses and the Tolbooth on the south, and which, being continued by a still narrower passage skirting the west end of St Giles’ Church, formed the western approach to the Parliament Close. Obeying his guidance in this tortuous passage, she soon found herself at the opening, or the square space—so styled on account of its being closed on more than one side by the meetingplace of the legislative assembly of Scotland. Here a splendid scene
awaited her. The whole square was filled with the members of the Scottish Parliament, Barons and Commons, who had just left the House in which they sat together,—with ladies, who on days of unusual ceremony were allowed to attend the House, and with horses richly caparisoned, and covered with gold-embroidered footcloths, some of which were mounted by their owners, while others were held in readiness by footmen. All was bustle and magnificence. Noblemen and gentlemen in splendid attire threaded the crowd in search of their horses; ladies tripped after them with timid and careful steps, endeavouring, by all in their power, to avoid contact with such objects as were calculated to injure their fineries; grooms strode heavily about, and more nimble lackeys jumped everywhere, here and there, some of them as drunk as the Parliament Close claret could make them, but all intent on doing the duties of attendance and respect to their masters. Some smart and well-dressed young gentlemen were arranging their cloaks and swords, and preparing to leave the square on foot, by the passage which had given entry to Master Richard and Lady Jean.
At sight of our heroine, most of these gallants stood still in admiration, and one of them, with the trained assurance of a rake, observing her to be beautiful, a stranger, and not too well protected, accosted her in a strain of language which caused her at once to blush and tremble. Richard’s brow reddened with anger as he hesitated not a moment in stepping up and telling the offender to leave the lady alone, on pain of certain consequences which might not prove agreeable.
“And who are you, my brave fellow?” said the youth, with bold assurance.
“Sirrah!” exclaimed Richard, so indignant as to forget himself, “I am that lady’s husband—her servant, I mean,” and here he stopped short in some confusion.
“Admirable!” exclaimed the other. “Ha! ha! ha! ha! Here, sirs, is a lady’s lacquey, who does not know whether he is his mistress’ servant or her husband. Let us give him up to the town-guard, to see whether the black hole will make him remember the real state of the case.”
So saying, he attempted to push Richard aside, and take hold of the lady. But he had not time to touch her garments with so much as a finger, before her protector had a rapier flourishing in his eyes, and
threatened him with instant death, unless he desisted from his profane purpose. At sight of the bright steel he stepped back one or two paces, drew his own sword, and was preparing to fight, when one of his more grave associates called out—
“For shame, Rollo!—with a lady’s lacquey, too, and in the presence of the duke and duchess! I see their royal highnesses, already alarmed, are inquiring the cause of the disturbance.”
It was even as this gentleman said, and presently came up to the scene of contention some of the most distinguished personages in the crowd, one of whom demanded from the parties an explanation of so disgraceful an occurrence.
“Why, here is a fellow, my lord,” answered Rollo, “who says he is the husband of a lady whom he attends as a liveryman, and a lady, too, the bonniest, I daresay, that has been seen in Scotland since the days of Queen Magdalen!”
“And what matters it to you,” said the inquirer, who seemed to be a judge of the Session, “in what relation this man stands to his lady? Let the parties both come forward, and tell their ain tale. May it please your royal highness,” he continued, addressing a very grave dignitary, who sat on horseback behind him, as stiff and formal as a sign-post, “to hear the declarator of thir twa strange incomers. But see—see—what is the matter wi’ Lord Wigton?” he added, pointing to an aged personage on horseback, who had just pushed forward, and seemed about to faint and fall from his horse. The person alluded to, at sight of his daughter in this unexpected place, was, in reality, confounded, and it was some time before he mastered voice enough to ejaculate—
“Oh, Jean, Jean! what is this ye’ve been about? or what has brocht you to Edinburgh?”
“Lord have a care of us!” exclaimed at this juncture another venerable peer, who had just come up, “what has brocht my sonsie son, Richie Livingstone, to Edinburgh, when he should have been fechtin’ the Dutch by this time in Transylvania?”
The two lovers, thus recognised by their respective parents, stood with downcast looks, and perfectly silent, while all was buzz and confusion in the brilliant circle around them; for the parties concerned were not more surprised at the aspect of their affairs, than
were all the rest at the beauty of the far-famed but hitherto unseen Lady Jean Fleming. The Earl of Linlithgow, Richard’s father, was the first to speak aloud, after the general astonishment had for some time subsided; and this he did in a laconic though important query, which he couched in the simple words,—
“Are ye married, bairns?”
“Yes, dearest father,” said his son, gathering courage, and coming close up to his saddle-bow; “and I beseech you to extricate Lady Jean and me from this crowd, and I shall tell you all when we are alone.”
“A pretty man ye are, truly,” said the old man, who never took anything very seriously to heart, “to be staying at hame, and getting yoursel married, all this time you should have been abroad, winning honour and wealth, as your gallant granduncle did wi’ Gustavus i’ the thretties! Hooever, since better mayna be, I maun try and console my Lord Wigton, who, I doot, has the worst o’ the bargain, ye ne’er-doweel!”
He then went up to Lady Jean’s father, shook him by the hand, and said, that “though they had been made relations against their wills, he hoped they would continue good friends. The young people,” he observed, “are no that ill-matched; and it is not the first time that the Flemings and the Livingstones have melled together, as witness the blithe marriage of the Queen’s Marie to Lord Fleming in the fifteensaxty-five. At ony rate, my lord, let us put a good face on the matter, afore thae glowerin’ gentles, and whipper-snapper duchesses. I’ll get horses for the two, and they’ll join the riding’ down the street; and de’il hae me, if Lady Jean doesna outshine the hale o’ them!”
“My Lord Linlithgow,” responded the graver and more implacable Earl of Wigton, “it may set you to take this matter blithely, but let me tell you, its a muckle mair serious affair for me. What think ye am I to do wi’ Frances and Grizzy noo?”
“Hoot toot, my lord,” said Linlithgow with a sly smile, “their chance is as gude as ever it was, I assure you, and sae will everybody think that kens them. I maun ca’ horses though, or the young folk will be ridden ower afore ever they do more gude, by thae rampaugin’ young men.” So saying, and taking Lord Wigton’s moody silence for assent, he proceeded to cry to his servants for the best pair of horses they could get, and these being speedily procured,
Lord Richard and his bride were requested to mount; after which they were formally introduced to the gracious notice of the Duke and Duchess of York, and the Princess Anne, who happened to attend Parliament on this the last day of its session, when it was customary for all the members to ride both to and from the House in an orderly cavalcade.
The order was given to proceed, and the lovers were soon relieved in a great measure from the embarrassing notice of the crowd, by assuming a particular place in the procession, and finding themselves confounded with more than three hundred equally splendid figures. As the pageant, however, moved down the High Street in a continuous and open line, it was impossible not to distinguish the singular loveliness of Lady Jean, and the gallant carriage of her husband, from all the rest. Accordingly, the trained bands and city guard, who lined the street, and who were in general quite as insensible to the splendours of “the Riding” as are the musicians in a modern orchestra to the wonders of a melodrama in its fortieth night,—even they perceived and admired the graces of the young couple, whom they could not help gazing after with a stupid and lingering delight. From the windows, too, and the “stair-heads,” their beauty was well observed, and amply conjectured and commented on; while many a young cavalier endeavoured, by all sorts of pretences, to find occasion to break the order of the cavalcade, and get himself haply placed nearer to the exquisite figure, of which he had got just one killing glance in the square. Slowly and majestically the brilliant train paced down the great street of Edinburgh—the acclamations of the multitude ceaselessly expressing the delight which the people of Scotland felt in this sensible type and emblem of their ancient independence.
At length they reached the courtyard of Holyrood-house, where the duke and duchess invited the whole assemblage to a ball, which they designed to give that evening in the hall of the palace; after which all departed to their respective residences throughout the town, Lords Wigton and Linlithgow taking their young friends under their immediate protection, and seeking the residence of the former nobleman, a little way up the Canongate. In riding thither, the lovers had leisure to explain to their parents the singular circumstances of
their union, and address enough to obtain unqualified forgiveness for their imprudence.
On alighting at Lord Wigton’s house, Lady Jean found her sisters confined to their rooms with headache, or some such serious indisposition, and in the utmost dejection on account of having been thereby withheld from the Riding of the Parliament. Their spirits, as may be supposed, were not much elevated, when, on coming forth in dishabille to welcome their sister, they learned that she had had the good fortune to be married before them. Their ill-luck was, however, irremediable, and so, making a merit of submitting to it, they condescended to be rather agreeable during the dinner and the afternoon. It was not long before all parties were perfectly reconciled to what had taken place; and by the time it was necessary to dress for the ball, the elder young ladies declared themselves so much recovered as to be able to accompany their happy sister.
The Earl of Linlithgow and his son then sent a servant for proper dresses, and prepared themselves for the occasion without leaving the house. When all were ready, a number of chairs were called to transport their dainty persons down the street. The news of Lady Jean’s arrival, and of her marriage, having now spread abroad, the court in front of the house, the alley, and even the open street, were crowded with people of all ranks, anxious to catch a passing glimpse of the heroine of so strange a tale. As her chair was carried along, a buzz of admiration from all who were so happy as to be near it, marked its progress. Happy, too, was the gentleman who had the good luck to be near her chair as it was set down at the palace-gate, and assist her in stepping from it upon the lighted pavement. From the outer gate, along the piazza of the inner court, and all the way up the broad staircase to the illuminated hall, two rows of noblemen and gentlemen formed a brilliant avenue, as she passed along, while a hundred plumed caps were doffed in honour of so much beauty, and as many youthful eyes glanced bright with satisfaction at beholding it. The object of all this attention tripped modestly along in the hand of the Earl of Linlithgow, acknowledging, with many a graceful flexure and undulation of person, the compliments of the spectators.
At length the company entered the spacious and splendid room in which the ball was to be held. At the extremity, opposite to the entry, upon an elevated platform, sat the three royal personages, all of
whom, on Lady Jean’s introduction, rose and came forward to welcome her and her husband to the entertainments of Holyrood, and to hope that her ladyship would often adorn their circle. In a short time the dancing commenced; and, amidst all the ladies who exhibited their charms and their magnificent attire in that captivating exercise, who was, either in person or dress, half so brilliant as Lady Jean?—Chambers’s Edin. Journal.
THE MONKEY: A SECOND PASSAGE IN THE LIFE OF WILLIAM M‘GEE, WEAVER
IN HAMILTON.
B R M , LL.D.
I dinna think that in a’ nature there’s a mair curiouser cratur than a monkey. I mak this observe frae being witness to an extraordinar’ event that took place in Hamilton, three or four days after my neverto-be-forgotten Battle of the Breeks.[6] Some even gaed the length to say that it was to the full mair curiouser than that affair, in sae far as the principal performer in the ae case was a rational man, whereas in the ither he was only a bit ape. But folk may talk as they like about monkeys, and cry them down for being stupid and mischievous, I for ane will no gang that length. Whatever they may be on the score of mischief, there can be nae doubt, that, sae far as gumption is concerned, they are just uncommon; and for wit and fun they would beat ony man black and blue. In fact, I dinna think that monkeys are beasts ava. I hae a half notion that they are just wee hairy men, that canna or rather winna speak, in case they may be made to work like ither folk, instead of leading a life of idleness.
6. See ante, p. 223.
But to the point. I ance had a monkey, ane of the drollest looking deevils ye ever saw. He was gayan big for a monkey, and was hairy a’ ower, except his face and his bit hurdies, which had a degree of bareness about them, and were nearly as saft as a lady’s loof. Weel, what think ye that I did wi’ the beastie? ’Od, man, I dressed him up like a Heelandman, and put a kilt upon him, and a lang-tailed red coat, and a blue bannet, which for security’s sake I tied, womanlike, below his chin, wi’ twa bits of yellow ribbon. I not only did this, but I
learnt him to walk upon his twa hinder legs, and to carry a stick in his right hand when he gaed out, the better to support him in his peregrinations. He was for a’ the world like a wee man in kilts—sae much sae, that when Glengarry, the great Heeland chieftain, wha happened to be at Hamilton on a visit to the Duke, saw him by chance, he swore by the powers that he was like ane o’ the Celtic Society, and that if I likit he would endeavour to get him admitted a member of that body. I thocht at the time that Glengarry was jokin’, but I hae since had gude reason for thinking that he was in real earnest, as Andrew Brand says that he and the Celts hae been like to cut ane anither’s throats, and that he micht mean this as an affront upon them. Hoosomever I maun do Glengarry the justice to say, that had he got my Nosey (that was his name) made a member, he wadna hae pruved the least witty or courageous o’ the society, and would hae dune nae disgrace to the chief’s recommendation.
But I am fleeing awa like a shuttle frae the subject on hand. Weel, it turned out in this manner, as ye shall hear. Ae afternoon towards the gloamin’, I was obligated to tak a stap down to the cross wi’ a web under my arm, which I had finished for Mr Weft, the muslin manufacturer. By way of frolic—a gayan foolish ane I allow—I brocht Nosey alang wi’ me. He had on, as for ordinar, his Heeland dress, and walkit behind me, wi’ the bit stick in his hand and his tail sticking out frae below his kilt, as if he had been my flunkey. It was, after a’, a queer sicht, and, as may be supposed, I drew a hale crowd o’ bairns after me, bawling out, “Here’s Willie M‘Gee’s monkey,” and giein’ him nits and gingerbread, and makin’ as muckle o’ the cratur as could be; for Nosey was a great favourite in the town, and everybody likit him for his droll tricks, and the way he used to girn, and dance, and tumble ower his head, to amuse them.
On entering Mr Weft’s shop, I faund it empty; there wasna leiving soul within. I supposed he had gane out for a licht; and being gayan familiar wi’ him, I took a stap ben to the back shop, leaving Nosey in the fore ane. I sat for twa or three minutes, but naebody made his appearance. At last the front door, which I had ta’en care to shut after me, opened, and I look’t to see what it could be, thinking that, nae doubt, it was Mr Weft, or his apprentice. It was neither the ane nor the ither, but a strong middle-aged, redfaced Heelandman, wi’ specks on, and wi’ a kilt and a bannet, by a’ the world like my
monkey’s. Now, what think ye Nosey was about a’ this time? He was sittin’ behind the counter upon the lang three-leggit stool that stood forenent Mr Weft’s desk, and was turning ower the leaves of his ledger wi’ a look which, for auld-fashioned sagaciousness, was wonderfu’ to behold. I was sae tickled at the sight that I paid nae sort of attention to the Heelandman, but continued looking frae the backshop at Nosey, lauching a’ the time in my sleeve—for I jaloused that some queer scene would tak place between the twa. And I wasna far wrang, for the stranger, takin’ out a pound frae his spleuchan, handed it ower to the monkey, and speered at him, in his droll norland deealect, if he could change a note. When I heard this, I thought I would hae lauched outright; and naething but sheer curiosity to see how the thing would end made me keep my gravity. It was plain that Donald had ta’en Nosey for ane of his ain countrymen —and the thing after a’ wasna greatly to be wondered at, and that for three reasons.
Firstly, the shop was rather darkish.
Secondly, the Heelandman had on specks, as I hae just said; and it was likely on this account that he was rather short-sighted; and
Thirdly, Nosey, wi’ his kilt, and bannet, and red coat, was to a’ intents and purposes as like a human creature as a monkey could weel be.
Nae sooner, then, had he got the note than he opened it out, and lookit at it wi’ his wee, glowrin’, restless een, as if to see that it wasna a forgery. He then shook his head like a doctor when he’s no very sure what’s wrang wi’ a person, but wants to mak it appear that he kens a’ about it—and continued in this style till the Heelandman’s patience began to get exhausted.
“Can ye no shange the note, old shentleman?” quo’ Donald. Nosey gied his head anither shake, and lookit uncommon wise.
“Is the note no goot, sir?” spake the Heelandman, a second time; but the cratur, instead of answering him, only gied anither of his wise shakes, as much as to say, “I’m no very sure about it.” At this Donald lost his temper. “If the note doesna please ye, sir,” quo’ he, “I’ll thank ye to gie me it back again, and I’ll gang to some ither place.” And he stretchit out his hand to tak haud o’t, when my frien’ wi’ the tail,
lifting up his stick, lent him sic a whack ower the fingers as made him pu’ back in the twinkling of an ee.
“Cot tamn ye, ye auld scoundrel,” said the man; “de ye mean to tak my money frae me?” And he lifted up a rung big eneugh to fell a stot, and let flee at the monkey; but Nosey was ower quick for him, and, jumping aside, he lichted on a shelf before ane could say Jock Robinson. Here he rowed up the note like a ba’ in his hand, and put it into his coat pouch like ony rational cratur. Not only this, but he mockit the Heelandman by a’ manner of means, shooting out his tongue at him, spitting at him, and girning at him wi’ his queer outlandish physiognomy. Then he would tak haud o’ his tail in his twa hands, and wag it at Donald, and steeking his nieves, he would seem to threaten him with a leatherin’! A’thegither he was desperate impudent, and eneugh to try the patience of a saunt, no to speak o’ a het-bluided Heelandman. It was gude for sair een to see how Donald behavit on this occasion. He raged like ane demented, misca’ing the monkey beyond measure, and swearing as mony Gaelic aiths as micht hae saired an ordinar man for a twalmonth. During this time, I never steered a foot, but keepit keekin’ frae the back shop upon a’ that was ganging on. I was highly delighted; and jalousing that Nosey was ower supple to be easily catched, I had nae apprehension for the event, and remained snug in my berth to see the upshot.
In a short time, in comes Mr Weft, wi’ a piece of lowing paper in his hand, that he had got from the next door to licht the shop; and nae sooner did Donald see him than he axed him for his note.
“What note, honest man?” said Mr Weft.
“Cot tamn,” quo’ Donald; “the note the auld scoundrel, your grandfater, stole frae me.”
“My grandfaither!” answered the ither wi’ amazement. “I am thinking, honest man, ye hae had a glass ower muckle. My grandfaither has been dead for saxteen years, and I ne’er heard tell till now that he was a fief.”
“Weel, weel, then,” quo’ the Heelandman, “I don’t care naething about it. If he’s no your grandfaither, he’ll be your faither, or your brither, or your cousin.”
“My faither or my brither, or my cousin!” repeated Mr Weft. “I maun tell ye plainly, frien’, that I hae neither faither, nor brither, nor
cousin of ony description, on this side of the grave. I dinna understand ye, honest man, but I reckon that ye hae sat ower lang at the whisky, and my advice to ye is to stap awa hame and sleep it aff.”
At this speech the Heelandman lost a’ patience, and lookit sae awfully fairce, that ance or twice I was on the nick of coming forrit, and explaining how matters really stood; but curiosity keepit me chained to the back shop, and I just thoucht I would bide a wee, and see how the affair was like to end.
“Pray, wha are you, sir?” said Donald, putting his hands in his sides, and looking through his specks upon Mr Weft, like a deevil incarnit. “Wha are you, sir, that daur to speak to me in this manner?”
“Wha am I?” said the ither, drapping the remnant of the paper, which was burnin’ close to his fingers, “I am Saunders Weft, manufacturer in Hamilton—that’s what I am.”
“And I am Tonald Campbell, piper’s sister’s son to his grace the great, grand Tuke of Argyll,” thundered out the Heelandman, wi’ a voice that was fearsome to hear.
“And what about that?” quo’ Mr Weft, rather snappishly, as I thocht. “If ye were the great, grand Duke of Argyll himsel, as ye ca’ him, I’ll no permit you to kick up a dust in my shop.”
“Ye scounrel,” said Donald, seizing Mr Weft by the throat, and shaking him till he tottered like an aspen leaf, “div ye mean to speak ill of his grace the Tuke of Argyll?” And he gied him anither shake— then, laying haud of his nose, he swore that he would pu’t as lang as a cow’s tail, if he didna that instant restore him his lost property. At this sicht I began to grue a’ ower, and now saw the needcessity of stapping ben, and saving my employer frae farther damage, bodily and itherwise. Nae sooner had I made my appearance than Donald let go his grip of Mr Weft’s nose, and the latter, in a great passion, cried out—
“William M‘Gee, I tak ye to witness what I hae sufferit frae this bluidthirsty Heelandman! It’s no to be endured in a Christian country. I’ll hae the law of him, that I will. I’ll be whuppit but I’ll hae amends, although it costs me twenty pounds!”
“What’s the matter?” quo’ I, pretending ignorance of the hale concern. “What, in the name of Nebuchadnezzar, has set ye thegither by the lugs?”
Then Mr Weft began his tale, how he had been collared and weel nigh thrappled in his ain shop;—then the ither tauld how, in the first place, Mr Weft’s grandfaither, as he ca’d Nosey, had stolen his note, and how, in the second place, Mr Weft himself had insulted the great, grand Duke of Argyll. In a word, there was a desperate kick-up between them, the ane threeping that he would tak the law of the ither immediately. Na, in this respect Donald gaed the greatest length, for he swore that, rather than be defeated, he wad carry his cause to the House of Lords, although it cost him thretty pounds sterling. I now saw it was time to put in a word.
“Hout-tout, gentlemen,” quo’ I, “what’s the use of a’ this clishmaclaver? Ye’ve baith gotten the wrang sow by the lug, or my name’s no William M‘Gee. I’ll wager ye a penny-piece, that my monkey Nosey is at the bottom of the business.”
Nae sooner had I spoken the word, than the twa, looking round the shop, spied the beastie sitting upon the shelf, girning at them, and putting out his tongue, and wiggle-waggling his walking stick ower his left elbow, as if he had been playing upon the fiddle. Mr Weft at this apparition set up a loud laugh; his passion left him in a moment, when he saw the ridiculous mistake that the Heelandman had fa’en into, and I thocht he would hae bursted his sides wi’ evendown merriment. At first, Donald lookit desperate angry, and, judging frae the way he was twisting about his mouth and rowing his een, I opined that he intended some deadly skaith to the monkey. But his gude sense, of which Heelandmen are no a’thegither destitute, got the better of his anger, and he roared and lauched like the very mischief. Nor was this a’, for nae sooner had he began to lauch, than the monkey did the same thing, and held its sides in preceesely the same manner, imitating his actions, in the maist amusin’ way imaginable. This only set Donald a-lauching mair than ever, and when he lifted up his nieve, and shook it at Nosey in a gudehumoured way, what think ye that the cratur did? ’Od, man, he took the note frae his pouch, whaur it lay rowed up like a ba’, and papping it at Donald, hit him as fairly upon the nose as if it had been shot out of a weel-aimed musket. There was nae resisting this. The haill three, or rather the haill four, for Nosey joined us, set up a loud lauch; and the Heelandman’s was the loudest of a’, showing that he was really a man of sense, and could tak a joke as weel as his neighbours.
When the lauchin’ had a wee subsided, Mr Campbell, in order to show that he had nae ill will to Mr Weft, axed his pardon for the rough way he had treated him, but the worthy manufacturer wadna hear o’t. “Houts, man,” quo’ he, “dinna say a word about it. It’s a mistak a’thegither, and Solomon himsel, ye ken, whiles gaed wrang.” Whereupon the Heelandman bought a Kilmarnock nicht-cap, price elevenpence ha’penny, frae Mr Weft, and paid him wi’ part of the very note that brocht on the ferlie I hae just been relating. But his gude wull didna end here, for he insisted on takin’ us a’—Nosey amang the lave—to the nearest public, where he gied us a frien’ly glass, and we keepit talking about monkeys, and what not, in a manner at ance edifying and amusing to hear.
THE LADDER DANCER.
Men should know why They write, and for what end; but note or text, I never know the word which will come next; So on I ramble, now and then narrating, Now pondering.—Byron.
It was a lovely evening in summer, when a crowd hallooing and shouting in the street of L——, a village of the north of Scotland, at once disturbed my reveries, and left me little leisure again to yield myself to their wayward dominion. In sooth, I had no pretence for indifference to a very singular spectacle of a something-like human being moving in mid-air; and although its saltatory gambols in this unusual situation could scarcely be called dancing, it was certainly intended to be like it, however little the resemblance might be approved. A something between a male and female in point of dress —a perfect hermaphrodite in regard to costume—had mounted herself on gigantic stilts, on which she hopped about, defying the secrecy even of the middle floors of the surrounding houses, and in some cases giving her a peep into the attic regions of less lofty domiciles. In this manner, stalking about from side to side, like a crane among the reeds, the very Diable Boiteux himself was never more inquisitive after the domestic concerns of his neighbours, or better fitted to explore them by his invisibility, than she was by her altitude. Her presence in mid-air, in more than one instance, was the subject of alarm to the sober inmates of the street, who, little suspicious of such intrusion, might perhaps be engaged in household cares which did not court observation, or had sunk into the relaxations of an undress, after the fatigues and heat of the day. Everywhere the windows might be heard thrown up with impatient haste,—the sash skirling and creaking in its ascent with the violence
of the effort, and immediately after, a head might be seen poked forward to explore the “whence” and “wherefore,”—in short, to ask in one word, if it could be so condensed, the meaning and purpose of this aërial visitor.
The more desultory occupations of a little village hold but loosely together the different classes of it. Master and servant approach more nearly,—the one is less elevated, and the other less depressed, than in great towns,—a show is at least as great a treat to the one as to the other, and there is nothing in their respective notions of decorum to repress their joyous feelings, while under the irresistible impulse of the inimitable Mr Punch, or of the demure and clumsy bear, treading a measure with the graces of a Mercandotti. In short, the more simple elements of a villager’s mind are, like their own more robust frames, more easily inflamed;—there is more excitable stuff about them, because they are less frequently subjected to the tear and wear of novelty, which towns constantly afford. The schoolmaster and the schoolboy alike pour out from the lowly strawroofed “academy,” with the same eager and breathless haste, to catch a first glance, or secure a favourable post. Syntax and arithmetic— blessed oblivion!—are for the moment forgotten. Think of the ecstacies of the little culprit, who was perhaps under the rod, if at that awful moment a troop of dancing dogs, with their full accompaniment of pipe and tabor, came under the school window, and was at once gladdened with a respite and a show. One moment watching the grim smile of the pedagogue; next lost in wonder at the accomplished puppets—nothing to disturb his bliss but the trammels of Concordance, or the intricacies of the Rule of Three.
But if mere novelty has such delights for the younger portion, to escape from the monotony of village life has not less charms for the graver class of its inhabitants. An old gentleman, evidently unmindful of his dishabille, popped his head forth of his casement, heedless of the red Kilmarnock in which it was bedight, and gazed with eager curiosity on the ambitious female who had now passed his lattice. He seemed to have caught a hint of the dereglement of his own costume, by remarking that of his female neighbour at the adjoining window, who exposed courageously the snowy ringlets which begirt the region of bumps and qualities, in place of the brown and glossy curls, which, till that ill-fated moment, were supposed to
have belonged to it.[7] He withdrew from sight with some precipitation, but whether in horror of his own recklessness, or in deference to the heedlessness of his neighbour, must for ever remain in doubt. Is it then strange if there was quite a revel-rout in the streets of the little village, when old and young alike responded to the wonder of the scene? To whatever quarter she passed, not a window was down; labour was suspended to witness feats which no labour of theirs could accomplish. Women, bearing with them the marks of the household toils in which they had been last engaged, stood at their doors, some with sarcastic, but all with curious gaze; while the sunburnt Piedmontoise at times danced on her stilts a kind of mock waltz, or hobbled from side to side, in ridicule, as it would seem, of the livelier measure and footing of the quadrille. When, mounted on the highest point of her stilts, she strided across the way, to collect or to solicit pence, the little urchins hanging about their mothers, clung more closely to them as she approached, and looked up to her, doubting and fearful, as fish are said to be scared by a passing cloud. She was most successful among the male spectators of the village. Her feats with them excited no feelings of rivalry, and their notions of decorum were not so easily disturbed as those of their helpmates, who, in refusing their contribution, never withhold their reprobation of such anti-Christian gambols.
7. I love to luxuriate in a note: it is like hunting in an unenclosed country. One word about the affectations of Graybeards. Among all the ten thousand reasons for their gray hairs, no one ever thought of years as being at least a probable cause. It is one of the very few hereditary peculiarities of physical constitution, which are loudly proclaimed and gladly seized, to apologise for the sin of hoary locks. Acute sorrow, or sudden surprise; indigestion that talismanic thing, the nerves love, speculation or anything, in short, are all approved theories to explain their first intrusion among the legitimate ringlets of male and female persons of “ no particular age. ” Even it is said that people have awoke gray who lay down under very different colours; of course, they had had a bad dream, or lain on the wrong side, but no conscientious perruquier could have sworn to their identity under such a metamorphosis. In short, gray hairs are purely accidental; they have nothing to do with years; and being deemed a misfortune, have from time immemorial been always spoken of with reverence, but nowhere that I can recollect are they spoken of with affection, save in the beautiful song, “John Anderson, my Jo,” where the kind-hearted wife invokes blessings on the frosty pow of her aged partner.
“Gae awa wi’ you, ye idle randie! Weel sets the like o’ sic misleard queans to gang about the country playing antics like a fule, to fules
like yoursel,” was the answer given by a middle-aged woman, who stood near me, to the boy who carried round a wooden platter for the halfpence, and who instantly retired, to save herself from the latter part of her own reproach, dragging with her a ragged little rogue, who begged hard to remain till the end of the exhibition. By this time the procession had reached the end of the street, where some of the better class of the inhabitants resided, and some preparations were made for a more elaborate spectacle. The swarthy Savoyard, who accompanied the ladder-dancer, after surveying the field, seemed to fix his station opposite to a respectable-looking house, whose liberality he evidently measured by its outward pretensions.
There is no state of helplessness equal to that of ignorance of the language in which a favour is to be craved, and you may estimate the proficiency of the foreigner in the intricacies of our own dialect by the obsequiousness of his smile, which he at once adapts to the purposes of solicitation, and of defence against insult and ridicule. While with a look of preparation he bustled about, to gain attention, he grinned and nodded to the windows which were occupied, while he held a ladder upright, and placing his hat at the bottom of it to receive the niggard bounty of the spectators, he stood at the back of it, supporting it with both his hands. The lady of the stilts now advanced, and resting on one of them, with considerable address lifted up the other and pushed it forward, with an action that seemed to denote something like a salutation, or obeisance,—a kind of aërial salaam. At this moment the hall-door was opened, and a portlylooking woman of middle-age, evidently the mistress of the household, came forward and planted herself on the broad landingplace of the stair. There was about this personage the round, full look which betokens ease and affluence; and the firm, steady step which argues satisfaction with our condition. She fixed herself on the doorstep with the solid perpendicularity of Pompey’s Pillar, and now and then turned round to some young girls who attended her, as if to chide them for mixing her up with so silly an exhibition.
I had supposed that the Piedmontoise would have laid aside her stilts when she ascended the ladder, but far from it, for in this consisted the singularity of the exhibition. She climbed the ladder, still mounted on them, then descended like a cat on the other side of it; she hopped down as she had hopped up, with equal steadiness
